7 ms·

I feel like the MCP conversation conflates too many things and everyone has strong assumptions that aren't always correct. The fundamental issue is between one-

by prohobo 6mo ago

I feel like the MCP conversation conflates too many things and everyone has strong assumptions that aren't always correct. The fundamental issue is between one-off vs. persistent access across sessions: - If you need to interact with a local app in a one-off session, then use CLI. - If you need to interact with an online service in a one-off session, then use their API. - If you need to interact with a local app in a persistent manner, and if that app provides an MCP server, use it. - If you need to interact with an online service in a persistent manner, and if that app provides an MCP server, use it. Whether the MCP server is implemented well is a whole other question. A properly configured MCP explains to the agent how to use it without too much context bloat. Not using a proper MCP for persistent access, and instead trying to describe the interaction yourself with skill files, just doesn't make any sense. The MCP owner should be optimizing the prompts to help the agent use it effectively. MCP is the absolute best and most effective way to integrate external tools into your agent sessions. I don't understand what the arguments are against that statement?

pavelbuild 6mo ago
[dead]
xyzzy123 6mo ago
My main complaint with mcp is that it doesn't compose well with other tools or code. Like if I want to pull 1000 jira tickets and do some custom analysis I can do that with cli or api just fine, but not mcp.
- prohobo 6mo ago
  Right, that feels like something you'd do with a script and some API calls. MCP is more for a back and forth communication between agent and app/service, or for providing tool/API awareness during other tasks. Like MCP for Jira would let the AI know it can grab tickets from Jira when needed while working on other things. I guess it's more like: the MCP isn't for us - it's for the agent to decide when to use.
  - xyzzy123 6mo ago
    I just find that e.g. cli tools scale naturally from tiny use cases (view 1 ticket) to big use cases (view 1000 tickets) and I don't have to have 2 ways of doing things. Where I DO see MCPs getting actual use is when the auth story for something (looking at you slack, gmail, etc) is so gimped out that basically, regular people can't access data via CLI in any sane or reasonable way. You have to do an oauth dance involving app approvals that are specifically designed to create a walled garden of "blessed" integrations. The MCP provider then helpfully pays the integration tax for you (how generous!) while ensuring you can't do inconvenient things like say, bulk exporting your own data. As far as I can tell, that's the _actual_ sweet spot for MCPs. They're sort of a technology of control, providing you limited access to your own data, without letting you do arbitrary compute. I understand this can be considered a feature if you're on the other side of the walled garden, or you're interested in certain kinds of enterprise control. As a programmer however I prefer working in open ecosystems where code isn't restricted because it's inconvenient to someone's business model.
    - hadlock 6mo ago
      >while ensuring you can't do inconvenient things like say, bulk exporting your own data I think this is the key; I want my analysts to be able to access 40% of the database they need to do their job, but not the other 60% parts that would allow them to dump the business-secrets part of the db, and start up business across the street. You can do this to some extent with roles etc but MCP in some ways is the data firewall as your last line of protection/auth.
    - SOLAR_FIELDS 6mo ago
      The auth angle is pretty interesting here. I spend a fair amount of time helping nontechnical people set up AI workflows in Claude Cowork and MCP works pretty well for giving them an isolated external system that I can tightly control their workflow guardrails but also interestingly give them the freedom to treat what IS exposed as a generic api automation tool. That combined with skills lets these non technical people string together zapier like workflows in natural language which is absolutely huge for the level of agency and autonomy it awards these people. So I find it quite interesting for the use case of providing auth encapsulated API access to systems that would normally require an engineer to unlock. The story around “wrap this REST API into a controlled variant only for the end users use case and allow them to complete auth challenges in every which way” has been super useful. Some of my mcp servers go through an oauth challenge response, others provide them guidance to navigate to the system and generate an api key and paste it into the server on initial connection.
- insin 6mo ago
  You can make it compose by also giving the agent the necessary tools to do so. I encountered a similar scenario using Atlassian MCP recently, where someone needed to analyse hundreds of Confluence child pages from the last couple of years which all used the same starter template - I gave the agent a tool to let it call any other tool in batch and expose the results for subsequent tools to use as inputs, rather than dumping it straight into the context (e.g. another tool which gives each page to a sub-agent with a structured output schema and a prompt with extraction instructions, or piping the results into a code execution tool). It turned what would have been hundreds of individual tool calls filling the context with multiple MBs of raw confluence pages, into a couple of calls returning relevant low-hundreds of KBs of JSON the agent could work further with.
  - xyzzy123 6mo ago
    Hmm, but you can't write a standard MCP (e.g. batch_tool_call) that calls other MCPs because the protocol doesn't give you a way to know what other MCPs are loaded in the runtime with you or any means to call them? Or have I got that wrong? So I guess you had to modify the agent harness to do this? or I guess you could use... mcp-cli ... ??
    - jmcodes 6mo ago
      I don't maintain this anymore but I experimented with this a while back: https://github.com/jx-codes/lootbox https://github.com/jx-codes/lootbox Essentially you give the agent a way to run code that calls MCP servers, then it can use them like any other API. Nowadays small bash/bun scripts and an MCP gateway proxy gets me the same exact thing. So yeah at some level you do have to build out your own custom functionality.
  - losvedir 6mo ago
    But in the context of this discussion, Atlassian has a CLI tool, acli. I'm not quite following why that wouldn't have worked here. As a normal CLI you have all the power you need over it, and the LLM could have used it to fetch all the relevant pages and save to disk, sample a couple to determine the regular format, and then write a script to extract out what they needed, right? Maybe I don't understand the use case you're describing.
- somnium_sn 6mo ago
  Give the model a REPL and let it compose MCP calls either by using tool calls structured output, doing string processing or piping it to a fast cheap model to provide structured output. This is the same as a CLI. Bash is nothing but a programming language and you can do the same approach by giving the model JavaScript and have it call MCP tools and compose them. If you do that you can even throw in composing it with CLis as well
- Twirrim 6mo ago
  Weird... I've been happily using Atlassian's MCP for this kind of thing just fine?
noodletheworld 6mo ago
> MCP is the absolute best and most effective way to integrate external tools into your agent sessions Nope. The best way to interact with an external service is an api. It was the best way before, and its the best way now. MCP doesn't scale and it has a bloated unnecessarily complicated spec. Some MCP servers are good; but in general a new bad way of interacting with external services, is not the best way of doing it, and the assertion that it is in general, best, is what I refer to as “works for me” coolaid. …because it probably does work well for you. …because you are using a few, good, MCP servers. However, that doesn't scale, for all the reasons listed by the many detractors of MCP. Its not that it cant be used effectively, it is that in general it is a solution that has been incompetently slapped on by many providers who dont appreciate how to do it well and even then, it scales badly. It is a bad solution for a solved problem. Agents have made the problem MCP was solving obsolete.
- prohobo 6mo ago
  Let's say I made a calendar app that stores appointments for you. It's local, installed on your system, and the data is stored in some file in ~/.calendarapp. Now let's say you want all your Claude Code sessions to use this calendar app so that you can always say something like "ah yes, do I have availability on Saturday for this meeting?" and the AI will look at the schedule to find out. What's the best way to create this persistent connection to the calendar app? I think it's obviously an MCP server. In the calendar app I provide a built-in MCP server that gives the following tools to agents: read_calendar, and update_calendar. You open Claude Code and connect to the MCP server, and configure it to connect to the MCP for all sessions - and you're done. You don't have to explain what the calendar app is, when to use it, or how to use it. Explain to me a better solution.
  - frotaur 6mo ago
    Why couldn't the calendar app expose in an API the read_calendar and update_calendar functionalities, and have a skill 'use_calendar' that describes how to use the above? Then, the minimal skill descriptions are always in the model's context, and whenever you ask it to add something to the calendar, it will know to fetch that skill. It feels very similar to the MCP solution to me, but with potentially less bloat and no obligation to deal with MCP? I might be missing something, though.
addandsubtract 6mo ago
Meanwhile, I'm using MCP for the LLM to lookup up-to-date documentation, and not hallucinate APIs.
CuriouslyC 6mo ago
MCP is less discoverable than a CLI. You can have detailed, progressive disclosure for a CLI via --help and subcommands. MCPs needs to be wrapped to be composed. MCPs needs to implement stateful behavior, shell + cli gives it to you for free. MCP isn't great, the main value of it is that it's got uptake, it's structured and it's "for agents." You can wrap/introspect MCP to do lots of neat things.
- prohobo 6mo ago
  "MCP is less discoverable than a CLI" - that doesn't make any sense in terms of agent context. Once an MCP is connected the agent should have full understanding of the tools and their use, before even attempting to use them. In order for the agent to even know about a CLI you need to guide the agent towards it - manually, every single session, or through a "skill" injection - and it needs to run the CLI commands to check them. "MCPs needs to implement stateful behavior" - also doesn't make any sense. Why would an MCP need to implement stateful behavior? It is essentially just an API for agents to use.
  - CuriouslyC 6mo ago
    If you have an API with thousands of endpoints, that MCP description is going to totally rot your context and make your model dumb, and there's no mechanism for progressive disclosure of parts of the tool's abilities, like there is for CLIs where you can do something like: tool --help tool subcommand1 --help tool subcommand2 --help man tool | grep "thing I care about" As for stateful behavior, say you have the google docs or email mcp. You want to search org-wide for docs or emails that match some filter, make it a data set, then do analysis. To do this with MCP, the model has to write the files manually after reading however many KB of input from the MCP. With a cli it's just "tool >> starting_data_set.csv"
    - prohobo 6mo ago
      This is a design problem, and not something necessarily solved by CLI --help commands. You can implement progressive disclosure in MCP as well by implementing those same help commands as tools. The MCP should not be providing thousands of tools, but the minimum set of tools to help the AI use the service. If your service is small, you can probably distill the entire API into MCP tools. If you're AWS then you provide tools that then document the API progressively. Technically, you could have an AWS MCP provide one tool that guides the AI on how to use specific AWS services through search/keywords and some kind of cursor logic. The entire point of MCP is inherent knowledge of a tool for agentic use.
mbreese 6mo ago
I think a lot of the MCP arguments conflate MCP the protocol versus how we currently discover and use MCP tool servers. I think there’s a lot of overhead and friction right now with how MCP servers are called and discovered by agents, but there’s no reason why it has to be that way. Honestly, an agent shouldn’t really care how it’s getting an answer, only that it’s getting an answer to the question it needs answered. If that’s a skill, API call, or MCP tool call, it shouldn’t really matter all that much to the agent. The rest is just how it’s configured for the users.
JamesSwift 6mo ago
Hard disagree. Apis and clis have been THOROUGHLY documented for human consumption for years and guess what, the models have that context already. Not only of the docs but actual in the wild use. If you can hook up auth for an agent, using any random external service is generally accomplished by just saying “hit the api”. I wrap all my apis in small bash wrappers that is just curl with automatic session handling so the AI only needs to focus on querying. The only thing in the -h for these scripts is a note that it is a wrapper around curl. I havent had a single issue with AI spinning its wheels trying to understand how to hit the downstream system. No context bloat needed and no reinventing the wheel with MCP when the api already exists
- anon84873628 6mo ago
  By wrapping the API with a script and feeding that inventory to the LLM... You reinvented MCP. Having service providers implement MCP saves everyone from having to do that work themselves. Plus there are a lot more uses cases than developers running agents on their own machine.
  - JamesSwift 6mo ago
    Wrapping here is literally just ``` #!/usr/bin/env bash creds={path to creds} basepath={url basepath} url={parse from args} curl -H "Authorization: #{creds}" "#{basepath}/#{url}" $rest_of_args ``` Just a way to read/set the auth and then calling curl. Its generalizable to nearly all apis out there. It requires no work by the provider and you can shape it however you need.
Aperocky 6mo ago
It's like saying it is very safe and nice to drive a F150 with half ton of water on the truck bed. How about driving the same truck without that half ton of water?
mbesto 6mo ago
The way I see it is more like this: - Skills help the LLM answer the "how" to interact with API/CLIs from your original prompt - API is what actually sends/receives the interaction/request - CLI is the actual doing / instruct set of the interaction/request - MCP helps the LLM understand what is available from the CLI and API They are all complementary.
Eldodi 6mo ago
There was a great presentation at the MCP Dev Summit last week explaining MCP vs CLI vs Skills vs Code Mode: https://www.figma.com/deck/H6k0YExi7rEmI8E6j6R0th/MCP-Dev-Summit---MCP-vs-Code-Mode-vs-Skills?node-id=4375-984 https://www.figma.com/deck/H6k0YExi7rEmI8E6j6R0th/MCP-Dev-Su...

by prohobo 6mo ago

pavelbuild 6mo ago

[dead]

xyzzy123 6mo ago

My main complaint with mcp is that it doesn't compose well with other tools or code. Like if I want to pull 1000 jira tickets and do some custom analysis I can do that with cli or api just fine, but not mcp.

prohobo 6mo ago
Right, that feels like something you'd do with a script and some API calls. MCP is more for a back and forth communication between agent and app/service, or for providing tool/API awareness during other tasks. Like MCP for Jira would let the AI know it can grab tickets from Jira when needed while working on other things. I guess it's more like: the MCP isn't for us - it's for the agent to decide when to use.
- xyzzy123 6mo ago
  I just find that e.g. cli tools scale naturally from tiny use cases (view 1 ticket) to big use cases (view 1000 tickets) and I don't have to have 2 ways of doing things. Where I DO see MCPs getting actual use is when the auth story for something (looking at you slack, gmail, etc) is so gimped out that basically, regular people can't access data via CLI in any sane or reasonable way. You have to do an oauth dance involving app approvals that are specifically designed to create a walled garden of "blessed" integrations. The MCP provider then helpfully pays the integration tax for you (how generous!) while ensuring you can't do inconvenient things like say, bulk exporting your own data. As far as I can tell, that's the _actual_ sweet spot for MCPs. They're sort of a technology of control, providing you limited access to your own data, without letting you do arbitrary compute. I understand this can be considered a feature if you're on the other side of the walled garden, or you're interested in certain kinds of enterprise control. As a programmer however I prefer working in open ecosystems where code isn't restricted because it's inconvenient to someone's business model.
  - hadlock 6mo ago
    >while ensuring you can't do inconvenient things like say, bulk exporting your own data I think this is the key; I want my analysts to be able to access 40% of the database they need to do their job, but not the other 60% parts that would allow them to dump the business-secrets part of the db, and start up business across the street. You can do this to some extent with roles etc but MCP in some ways is the data firewall as your last line of protection/auth.
  - SOLAR_FIELDS 6mo ago
    The auth angle is pretty interesting here. I spend a fair amount of time helping nontechnical people set up AI workflows in Claude Cowork and MCP works pretty well for giving them an isolated external system that I can tightly control their workflow guardrails but also interestingly give them the freedom to treat what IS exposed as a generic api automation tool. That combined with skills lets these non technical people string together zapier like workflows in natural language which is absolutely huge for the level of agency and autonomy it awards these people. So I find it quite interesting for the use case of providing auth encapsulated API access to systems that would normally require an engineer to unlock. The story around “wrap this REST API into a controlled variant only for the end users use case and allow them to complete auth challenges in every which way” has been super useful. Some of my mcp servers go through an oauth challenge response, others provide them guidance to navigate to the system and generate an api key and paste it into the server on initial connection.
insin 6mo ago
You can make it compose by also giving the agent the necessary tools to do so. I encountered a similar scenario using Atlassian MCP recently, where someone needed to analyse hundreds of Confluence child pages from the last couple of years which all used the same starter template - I gave the agent a tool to let it call any other tool in batch and expose the results for subsequent tools to use as inputs, rather than dumping it straight into the context (e.g. another tool which gives each page to a sub-agent with a structured output schema and a prompt with extraction instructions, or piping the results into a code execution tool). It turned what would have been hundreds of individual tool calls filling the context with multiple MBs of raw confluence pages, into a couple of calls returning relevant low-hundreds of KBs of JSON the agent could work further with.
- xyzzy123 6mo ago
  Hmm, but you can't write a standard MCP (e.g. batch_tool_call) that calls other MCPs because the protocol doesn't give you a way to know what other MCPs are loaded in the runtime with you or any means to call them? Or have I got that wrong? So I guess you had to modify the agent harness to do this? or I guess you could use... mcp-cli ... ??
  - jmcodes 6mo ago
    I don't maintain this anymore but I experimented with this a while back: https://github.com/jx-codes/lootbox https://github.com/jx-codes/lootbox Essentially you give the agent a way to run code that calls MCP servers, then it can use them like any other API. Nowadays small bash/bun scripts and an MCP gateway proxy gets me the same exact thing. So yeah at some level you do have to build out your own custom functionality.
- losvedir 6mo ago
  But in the context of this discussion, Atlassian has a CLI tool, acli. I'm not quite following why that wouldn't have worked here. As a normal CLI you have all the power you need over it, and the LLM could have used it to fetch all the relevant pages and save to disk, sample a couple to determine the regular format, and then write a script to extract out what they needed, right? Maybe I don't understand the use case you're describing.
somnium_sn 6mo ago
Give the model a REPL and let it compose MCP calls either by using tool calls structured output, doing string processing or piping it to a fast cheap model to provide structured output. This is the same as a CLI. Bash is nothing but a programming language and you can do the same approach by giving the model JavaScript and have it call MCP tools and compose them. If you do that you can even throw in composing it with CLis as well
Twirrim 6mo ago
Weird... I've been happily using Atlassian's MCP for this kind of thing just fine?

noodletheworld 6mo ago

> MCP is the absolute best and most effective way to integrate external tools into your agent sessions Nope. The best way to interact with an external service is an api. It was the best way before, and its the best way now. MCP doesn't scale and it has a bloated unnecessarily complicated spec. Some MCP servers are good; but in general a new bad way of interacting with external services, is not the best way of doing it, and the assertion that it is in general, best, is what I refer to as “works for me” coolaid. …because it probably does work well for you. …because you are using a few, good, MCP servers. However, that doesn't scale, for all the reasons listed by the many detractors of MCP. Its not that it cant be used effectively, it is that in general it is a solution that has been incompetently slapped on by many providers who dont appreciate how to do it well and even then, it scales badly. It is a bad solution for a solved problem. Agents have made the problem MCP was solving obsolete.

prohobo 6mo ago
Let's say I made a calendar app that stores appointments for you. It's local, installed on your system, and the data is stored in some file in ~/.calendarapp. Now let's say you want all your Claude Code sessions to use this calendar app so that you can always say something like "ah yes, do I have availability on Saturday for this meeting?" and the AI will look at the schedule to find out. What's the best way to create this persistent connection to the calendar app? I think it's obviously an MCP server. In the calendar app I provide a built-in MCP server that gives the following tools to agents: read_calendar, and update_calendar. You open Claude Code and connect to the MCP server, and configure it to connect to the MCP for all sessions - and you're done. You don't have to explain what the calendar app is, when to use it, or how to use it. Explain to me a better solution.
- frotaur 6mo ago
  Why couldn't the calendar app expose in an API the read_calendar and update_calendar functionalities, and have a skill 'use_calendar' that describes how to use the above? Then, the minimal skill descriptions are always in the model's context, and whenever you ask it to add something to the calendar, it will know to fetch that skill. It feels very similar to the MCP solution to me, but with potentially less bloat and no obligation to deal with MCP? I might be missing something, though.

addandsubtract 6mo ago

Meanwhile, I'm using MCP for the LLM to lookup up-to-date documentation, and not hallucinate APIs.

CuriouslyC 6mo ago

MCP is less discoverable than a CLI. You can have detailed, progressive disclosure for a CLI via --help and subcommands. MCPs needs to be wrapped to be composed. MCPs needs to implement stateful behavior, shell + cli gives it to you for free. MCP isn't great, the main value of it is that it's got uptake, it's structured and it's "for agents." You can wrap/introspect MCP to do lots of neat things.

prohobo 6mo ago
"MCP is less discoverable than a CLI" - that doesn't make any sense in terms of agent context. Once an MCP is connected the agent should have full understanding of the tools and their use, before even attempting to use them. In order for the agent to even know about a CLI you need to guide the agent towards it - manually, every single session, or through a "skill" injection - and it needs to run the CLI commands to check them. "MCPs needs to implement stateful behavior" - also doesn't make any sense. Why would an MCP need to implement stateful behavior? It is essentially just an API for agents to use.
- CuriouslyC 6mo ago
  If you have an API with thousands of endpoints, that MCP description is going to totally rot your context and make your model dumb, and there's no mechanism for progressive disclosure of parts of the tool's abilities, like there is for CLIs where you can do something like: tool --help tool subcommand1 --help tool subcommand2 --help man tool | grep "thing I care about" As for stateful behavior, say you have the google docs or email mcp. You want to search org-wide for docs or emails that match some filter, make it a data set, then do analysis. To do this with MCP, the model has to write the files manually after reading however many KB of input from the MCP. With a cli it's just "tool >> starting_data_set.csv"
  - prohobo 6mo ago
    This is a design problem, and not something necessarily solved by CLI --help commands. You can implement progressive disclosure in MCP as well by implementing those same help commands as tools. The MCP should not be providing thousands of tools, but the minimum set of tools to help the AI use the service. If your service is small, you can probably distill the entire API into MCP tools. If you're AWS then you provide tools that then document the API progressively. Technically, you could have an AWS MCP provide one tool that guides the AI on how to use specific AWS services through search/keywords and some kind of cursor logic. The entire point of MCP is inherent knowledge of a tool for agentic use.

mbreese 6mo ago

I think a lot of the MCP arguments conflate MCP the protocol versus how we currently discover and use MCP tool servers. I think there’s a lot of overhead and friction right now with how MCP servers are called and discovered by agents, but there’s no reason why it has to be that way. Honestly, an agent shouldn’t really care how it’s getting an answer, only that it’s getting an answer to the question it needs answered. If that’s a skill, API call, or MCP tool call, it shouldn’t really matter all that much to the agent. The rest is just how it’s configured for the users.

JamesSwift 6mo ago

Hard disagree. Apis and clis have been THOROUGHLY documented for human consumption for years and guess what, the models have that context already. Not only of the docs but actual in the wild use. If you can hook up auth for an agent, using any random external service is generally accomplished by just saying “hit the api”. I wrap all my apis in small bash wrappers that is just curl with automatic session handling so the AI only needs to focus on querying. The only thing in the -h for these scripts is a note that it is a wrapper around curl. I havent had a single issue with AI spinning its wheels trying to understand how to hit the downstream system. No context bloat needed and no reinventing the wheel with MCP when the api already exists

anon84873628 6mo ago
By wrapping the API with a script and feeding that inventory to the LLM... You reinvented MCP. Having service providers implement MCP saves everyone from having to do that work themselves. Plus there are a lot more uses cases than developers running agents on their own machine.
- JamesSwift 6mo ago
  Wrapping here is literally just ``` #!/usr/bin/env bash creds={path to creds} basepath={url basepath} url={parse from args} curl -H "Authorization: #{creds}" "#{basepath}/#{url}" $rest_of_args ``` Just a way to read/set the auth and then calling curl. Its generalizable to nearly all apis out there. It requires no work by the provider and you can shape it however you need.

Aperocky 6mo ago

It's like saying it is very safe and nice to drive a F150 with half ton of water on the truck bed. How about driving the same truck without that half ton of water?

mbesto 6mo ago

The way I see it is more like this: - Skills help the LLM answer the "how" to interact with API/CLIs from your original prompt - API is what actually sends/receives the interaction/request - CLI is the actual doing / instruct set of the interaction/request - MCP helps the LLM understand what is available from the CLI and API They are all complementary.

Eldodi 6mo ago

There was a great presentation at the MCP Dev Summit last week explaining MCP vs CLI vs Skills vs Code Mode: https://www.figma.com/deck/H6k0YExi7rEmI8E6j6R0th/MCP-Dev-Summit---MCP-vs-Code-Mode-vs-Skills?node-id=4375-984 https://www.figma.com/deck/H6k0YExi7rEmI8E6j6R0th/MCP-Dev-Su...