Product MCP server for cotal.ai, the open protocol that lets AI agents from any vendor work as one team. Tools: product overview, site search, build-log posts, feedback, Cotal Cloud waitlist, newsletter and call requests. No credentials needed.
Each tool has a clearly distinct purpose: getting a single post, listing posts, searching, joining waitlist, requesting a call, submitting feedback, subscribing, and getting overview. No overlaps or ambiguous boundaries.
All tools follow the same 'cotal_' prefix and snake_case verb_noun pattern (get_post, list_posts, join_waitlist, etc.), making naming highly consistent and predictable.
8 tools is well-scoped for a product site with content browsing, engagement actions, and feedback. Not sparse, not overwhelming.
The tool set covers the full user journey: discovering content (list, search, get), understanding the product (overview), and taking actions (waitlist, call, feedback, subscribe). No obvious gaps for the intended domain.
Returns one build-log post as markdown (title, summary, date, author, full body) by its slug. Get slugs from cotal_list_posts or cotal_search.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | The post slug, e.g. 'agi-summit-2026'. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description matches the annotations: readOnlyHint=true, idempotentHint=true, destructiveHint=false. It states that the tool 'returns' data, implying no side effects. The description adds context by specifying the exact output format (title, summary, date, author, full body), which goes beyond the annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise, using only two sentences to convey purpose, output format, and slug source. There is no redundant or filler content, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simplicity of the tool, the description is complete. It specifies the output format, input parameter, and the source of valid slugs. No output schema is present, but the markdown fields are explicitly listed, so an agent knows exactly what to expect.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The single parameter slug is well-described in both the schema and the description, with a concrete example. Since the schema already covers the parameter with 100% coverage, the description adds modest extra clarity with the example, justifying a score above the baseline of 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: returning a single build-log post in markdown format by slug. It also differentiates from siblings by explaining that slugs come from cotal_list_posts or cotal_search, distinguishing it from those tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description tells users where to obtain the slug (list_posts or search), which is useful guidance. It implicitly indicates that this is the tool to use when you already know the slug and need the full post content. It could be slightly more explicit about when not to use it, but the guidance is sufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Reserves a seat on the waitlist for Cotal Cloud, the hosted Cotal mesh (closed alpha), for the user's email. Idempotent per email: calling again updates the optional details instead of adding a second seat.
| Name | Required | Description | Default |
|---|---|---|---|
| Yes | The user's email address. Required so the record has a traceable origin. | ||
| sandbox | No | Dry run: validate the call, apply rate limits, store nothing. Use it to test the integration. | |
| No | The user's LinkedIn profile URL, optional. | ||
| use_case | No | What the user wants the hosted mesh for. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description goes beyond annotations by explaining the idempotent nature of the call and the behavior of the sandbox parameter (validate, apply rate limits, store nothing). This discloses side effects and testing behavior clearly.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise—just two sentences—and front-loads the primary purpose, followed by the idempotency clarification. No redundant or filler content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simple parameter set and no output schema, the description covers all essential usage aspects: the action, idempotency, sandbox mode, and parameter semantics. Nothing is missing for an agent to invoke this correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
All parameters are described with meaningful context (e.g., email is 'Required so the record has a traceable origin', sandbox is 'Dry run'). The descriptions add nuance beyond the schema format, such as the purpose of the email and the sandbox's effect.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Reserves a seat on the waitlist'), the resource ('Cotal Cloud, the hosted Cotal mesh'), and the target audience ('for the user's email'). It is specific and distinct from sibling tools like subscribe_newsletter or request_call.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides practical usage guidance by noting idempotency ('calling again updates the optional details instead of adding a second seat') and the sandbox dry-run mode. It does not explicitly contrast with sibling tools, but the purpose is clear enough that an agent would infer when to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Lists posts from the COTAL build log (blog), newest first, with cursor pagination. Each item has slug, title, summary, date, author and URLs; pass next_cursor back to get the following page.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Page size (default 10, max 50). | |
| cursor | No | The next_cursor value from a previous call. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With annotations already declaring readOnlyHint, idempotentHint, and destructiveHint false, the description adds valuable behavioral context: ordering (newest first), cursor pagination mechanics, and the item fields returned. This goes well beyond the annotations and gives the agent a complete mental model of the call.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences carry all essential information: what is listed, ordering, pagination, and response item structure. There is no redundant or filler content, and the most important details are front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity, full schema coverage, and thorough annotations, the description is complete. It covers the return shape, pagination behavior, and read-only nature, leaving no critical gap for an agent deciding whether and how to call it.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, and both limit and cursor are described in the schema. The description reinforces the cursor mechanism by mentioning next_cursor, but it adds little meaning beyond what the schema already provides, so a baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool lists posts from the COTAL build log, newest first, with pagination. This distinguishes it from siblings like cotal_get_post (single post) and cotal_search (searching), so an agent can confidently identify its purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context: use it to browse build-log posts and paginate with next_cursor. It does not explicitly name alternative tools or state when not to use it, but the context is strong enough for a routine list endpoint.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Returns a structured overview of COTAL: what it is, who it is for, when to use it, how to install it, the site's API and MCP surface, pricing, and canonical links. Call this first when a user asks what cotal.ai does. Pass section to get one part only.
| Name | Required | Description | Default |
|---|---|---|---|
| section | No | Which part of the overview to return (default: all). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already declare readOnly, idempotent, and non-destructive behavior; the description does not add any additional behavioral details beyond the fact that it returns an overview, which is consistent with the annotations. No contradiction, but also no extra transparency beyond what the annotations provide.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, concise and well-structured, listing the content areas without any extraneous information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity, the description provides sufficient context: what it returns, when to call it, and how to use the optional parameter. No output schema is needed, and no edge cases are relevant.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The single parameter 'section' is fully described in the schema with an enum and description, and the tool description adds the clarification that passing section retrieves only one part, reinforcing the default behavior.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool returns a structured overview of COTAL and enumerates the contents (what it is, who it's for, etc.), and also instructs to call it first when asked what cotal.ai does, distinguishing it from sibling tools that perform specific actions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It explicitly says 'Call this first when a user asks what cotal.ai does' and explains how to use the section parameter to retrieve a single part, giving clear guidance on when to use this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Sends a discovery-call request to the COTAL founders for a company evaluating agent coordination. The team replies by email. Requires the user's work email and company.
| Name | Required | Description | Default |
|---|---|---|---|
| pain | No | Their biggest coordination pain. | |
| role | No | The user's role. | |
| Yes | The user's email address. Required so the record has a traceable origin. | ||
| agents | No | How many agents the company runs in production. | |
| company | Yes | Company name. | |
| sandbox | No | Dry run: validate the call, apply rate limits, store nothing. Use it to test the integration. | |
| frameworks | No | Agent frameworks or harnesses in use (Claude Code, LangGraph, custom…). | |
| coordinating | No | One line: what they are coordinating. | |
| idempotency_key | No | Opaque key (a UUID is ideal). Reuse it when retrying so the same record is returned instead of a duplicate. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the annotations (which indicate non-read-only, non-idempotent, non-destructive), the description adds that 'The team replies by email' as a consequence. It also clarifies the prerequisite of work email and company. This provides useful context about the side effects and requirements, though it does not detail storage or retry behavior beyond the schema.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence that packs all essential information without redundancy. It flows well and avoids unnecessary details, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (9 parameters, no output schema), the description provides adequate context by stating the action, the recipient, the purpose, and the reply mechanism. It does not specify return values or error handling, but the absence of an output schema and the straightforward nature of the action make this sufficient. The idempotency key behavior is already covered in the schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already has 100% coverage with descriptive explanations for all parameters (e.g., email's 'traceable origin', idempotency_key's reuse behavior). The description adds a small clarification that email is a work email and company is required, which slightly enhances meaning beyond the schema. Overall, the parameter semantics are well-covered.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action: 'Sends a discovery-call request to the COTAL founders'. It specifies the resource (COTAL founders) and the purpose (for a company evaluating agent coordination). This distinguishes it from siblings like cotal_submit_feedback or cotal_join_waitlist.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description mentions a prerequisite: 'Requires the user's work email and company.' However, it does not explicitly state when to use this tool versus alternatives, such as using cotal_join_waitlist for a different type of interest or cotal_submit_feedback for feedback. The guidance is implicit but not fully explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Keyword search over the COTAL build log (blog posts) and the key documentation and site pages. Returns ranked results with canonical and markdown URLs. Use it to find the page that answers a question before reading it.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Maximum results (default 10). | |
| query | Yes | Search terms, e.g. 'hackathon', 'identity', 'install windows'. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, non-destructive, idempotent behavior. Description adds that results include canonical and markdown URLs, but doesn't disclose any extra side effects or restrictions, so it relies on annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, each carrying essential information: scope and return value with a use case. No redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Describes return type (ranked results with canonical and markdown URLs) but not the exact structure, ordering, or how to interpret ranking. Given no output schema, this is moderately complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema already describes both parameters with full coverage (query and limit). Description adds no further parameter semantics beyond calling it a keyword search.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states it performs keyword search over a specific corpus (build log, docs, site pages) and distinguishes from siblings like cotal_list_posts or cotal_get_post by focusing on search rather than listing or retrieving single posts.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides an explicit use case: 'find the page that answers a question before reading it.' It doesn't name alternative tools, but the context makes it clear when to use search versus listing or fetching.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Files feedback (bug, idea, friction, praise, other) with the COTAL team on behalf of the user. The same log the site's feedback widget writes to. Requires the user's email so the team can follow up.
| Name | Required | Description | Default |
|---|---|---|---|
| area | No | Which part of COTAL it concerns (CLI, connector, docs, site…). | |
| name | No | The user's name, if they want it attached. | |
| type | Yes | Kind of feedback. | |
| Yes | The user's email address. Required so the record has a traceable origin. | ||
| source | No | Where it came from: agent name, page, or CLI. | |
| details | No | Longer description, steps, context. | |
| sandbox | No | Dry run: validate the call, apply rate limits, store nothing. Use it to test the integration. | |
| summary | Yes | One line: what happened, or what should. | |
| severity | No | For bugs and friction. | |
| idempotency_key | No | Opaque key (a UUID is ideal). Reuse it when retrying so the same record is returned instead of a duplicate. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses that feedback is filed and persisted to the same log as the widget, and that an email is required. Annotations only indicate non-read-only and non-destructive; the description adds the key side-effect context. Sandbox and idempotency behaviors are handled in schema parameter descriptions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two concise sentences with no fluff. It front-loads the primary action and then adds the most important context (same log, email requirement).
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description is complete enough for a feedback-submission tool: it states purpose, persistence location, and the key prerequisite (email). No output schema exists, so return details are not needed. Optional sandbox and severity behaviors are fully covered in the schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% with detailed parameter descriptions, so the baseline is 3. The prose adds only the email follow-up rationale, which is already implied by the schema's 'traceable origin' description. No meaningful parameter semantics are added beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Files feedback'), the target (COTAL team), and enumerates the feedback types. It also distinguishes this tool from siblings by noting it writes to the same log as the site's feedback widget.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives useful context: it acts on behalf of the user, requires an email for follow-up, and uses the same log as the feedback widget. It does not explicitly contrast with alternatives, but the sibling tools are distinct enough that the intended use is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Subscribes the user's email to release updates from the COTAL team. Idempotent: an already-subscribed email is a no-op.
| Name | Required | Description | Default |
|---|---|---|---|
| Yes | The user's email address. Required so the record has a traceable origin. | ||
| sandbox | No | Dry run: validate the call, apply rate limits, store nothing. Use it to test the integration. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Description adds the no-op behavior for duplicate emails, which supplements the idempotentHint annotation. It also implies a write operation (subscribe) which aligns with readOnlyHint false. However, it does not detail other side effects like confirmation emails or rate limiting.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Description is extremely concise: one sentence for purpose and one for idempotency. No fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simple parameter set (two fields, one required), the description and schema together fully cover the tool's behavior. No output schema is needed, and the description does not omit necessary context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema provides full coverage for both parameters: email has pattern and maxLength; sandbox has a clear description explaining dry-run behavior. The tool description adds no additional parameter semantics, so baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the action: 'Subscribes the user's email to release updates from the COTAL team.' It identifies the resource (email) and the action (subscribe). However, it does not explicitly differentiate from similar sibling tools like cotal_join_waitlist, so it misses the top score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides guidance on idempotency: 'Idempotent: an already-subscribed email is a no-op.' But it does not state when to use this over other subscription or list tools, nor any prerequisites. Thus limited guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
cotal_get_postcotal_join_waitlistcotal_list_postscotal_overviewcotal_request_callcotal_searchcotal_submit_feedbackcotal_subscribe_newsletterClaiming proves that you control a remote MCP connector. It does not move, proxy, or interrupt the server.
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