Fathom AI MCP
Server Quality Checklist
Latest release: v0.2.0
- Disambiguation5/5
Each tool targets a distinct resource or action: summaries, transcripts, meetings list, team members, teams. No overlap in purpose.
Naming Consistency5/5All tools follow a consistent verb_noun pattern (get_*, list_*), making the set predictable and easy to navigate.
Tool Count5/5With 5 tools, the surface is well-scoped for a meeting information retrieval server, covering the core read operations without excess.
Completeness4/5The tools cover listing and retrieving meeting details, transcripts, and team info. Minor gaps like search or filtering capabilities might exist but do not hinder basic workflows.
Average 3.4/5 across 5 of 5 tools scored. Lowest: 2.8/5.
See the Tool Scores section below for per-tool breakdowns.
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How is the quality score calculated?
The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).
Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.
Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).
Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.
Tool Scores
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It does not mention pagination, result format, or side effects, leaving the agent uncertain about how the tool behaves beyond listing.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single short sentence, which is concise but lacks detail beyond the name and a basic filter. It is adequate but does not earn its place with extra value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations, no output schema, and three parameters, the description is incomplete. It fails to explain pagination behavior or result expectations, which are critical for tool selection and invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The description adds minor context by noting the team filter is optional, but this is already implied by the schema's non-required parameter. No significant additional meaning beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'list' and the resource 'team members', with an optional filter by team name. It is specific but does not explicitly differentiate from sibling tools like 'list_teams', which lists teams rather than members.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives, such as when to filter vs retrieve all members, nor are any exclusions or prerequisites mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description bears full responsibility for behavioral disclosure. It only states 'List all teams,' omitting any indication of pagination (though schema covers it), potential rate limits, authentication requirements, or data freshness. The description adds no behavioral context beyond the minimal verb and resource.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise at just four words, with no extraneous information. While effective, it could benefit from a bit more context (e.g., 'paginated list') without becoming verbose. The front-loading is good.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simple nature of a list tool with pagination handled by the schema, the description is adequate for basic use. However, it lacks details on output format (e.g., returns an array of team objects) and does not differentiate from sibling list tools like 'list_meetings', making it slightly less complete for an agent that needs to decide between them.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% coverage for both parameters (page and page_size) with clear descriptions and defaults. The tool description adds no additional meaning beyond what the schema already provides, so the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description 'List all teams' clearly specifies the verb (list) and resource (teams), indicating a straightforward retrieval operation. However, it doesn't differentiate from sibling tools like 'list_team_members' or 'list_meetings', which also list resources, but the distinct resource name makes it unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives such as 'get_summary' or 'list_team_members'. There is no mention of context, prerequisites, or exclusions, leaving the agent to infer usage without help.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It only mentions the output format (markdown without playback URLs), but does not cover idempotency, authorization needs, rate limits, or any side effects. This is insufficient for a retrieval tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, no redundant words. Every sentence contributes meaning: the first states the action and resource, the second details the output. Ideal conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has one simple parameter and no output schema. The description explains the input origin and output format, which is adequate but not exhaustive. It lacks details on limits (max recordings) or whether results are paginated, but is sufficient for basic usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with one parameter. The description adds value by noting that recording IDs come from 'list_meetings', which clarifies the parameter source beyond the schema's own description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Get summaries' and the resource 'meeting recordings', and specifies the output format (clean markdown with playback URLs stripped). It implicitly differentiates from the sibling 'get_transcript' by focusing on summaries rather than verbatim transcript, but does not explicitly contrast them.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives like 'get_transcript' or other siblings. There are no prerequisites or context for appropriate usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the burden of behavioral disclosure. It mentions timestamps are UTC and implies pagination, but does not disclose whether the tool is read-only, rate limits, or any destructive aspects. The description is adequate but lacks deeper behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the main purpose and features, and provides guidance in the second. Every word adds value, with no redundancy. Highly concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 11 documented parameters and no output schema, the description is minimal. It covers the basic purpose and points to alternatives, but lacks details on return format, pagination details (e.g., total pages), or ordering. For a complex filtering tool, more context would be helpful, but it is not incomplete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so baseline is 3. The description adds no additional parameter meaning beyond what the schema already provides (e.g., 'optional filtering and pagination' is already evident from the schema). It does not enhance parameter understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool lists meetings with optional filtering and pagination, and distinguishes from siblings by directing to get_summary/get_transcript for detailed content. The verb 'list' and resource 'meetings' are explicit.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description advises using get_summary or get_transcript for detailed content, indicating when not to use this tool. However, it does not address when to use this tool versus other sibling tools like list_teams, though those are for different resources. The guidance is clear but lacks explicit exclusions for all alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description discloses key behaviors: returns plaintext lines, metadata on first call without time params, and ability to request chunks. Lacks details on error handling or processing state, but sufficient for a read-only tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Five sentences total, no redundancy. Front-loaded with main purpose, followed by concise usage notes. Every sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema, so description should detail return structure. It says 'plaintext lines' and hints at metadata fields (duration, line count) but does not fully specify format or what 'plaintext lines' means. Adequate but could be more complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, and the description adds significant value by explaining the usage pattern (first call for metadata, then chunks). For recording_id, it references list_meetings, consistent with schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states the function: 'Get the transcript for a meeting recording. Returns plaintext lines.' Distinguishes well from sibling tools like get_summary and list_meetings.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit guidance on using start_time/end_time for long meetings and a two-call strategy to first fetch metadata. Does not explicitly contrast with siblings, but the context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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