Pydantic AI Documentation Server

by omniwaifu

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Latest release: v0.1.0
Disambiguation5/5
Each tool has a clearly distinct purpose with no overlap: get_changelog_content retrieves specific changelog content, get_document_by_path retrieves general documents, list_available_changelogs lists changelog files, list_topics lists general documentation topics, and update_documentation handles repository maintenance and indexing. The descriptions clearly differentiate their scopes and use cases.
Naming Consistency4/5
The naming follows a consistent verb_noun pattern with snake_case throughout (e.g., get_changelog_content, list_available_changelogs). The only minor deviation is update_documentation, which uses a verb_noun structure but lacks a direct object like the others, though it remains readable and fits the pattern well overall.
Tool Count5/5
With 5 tools, the count is well-scoped for a documentation server, covering key operations like retrieving documents, listing content, and updating the repository. Each tool earns its place without feeling excessive or insufficient for the domain of documentation management.
Completeness4/5
The tool set provides good coverage for documentation retrieval and management, including reading, listing, and updating. A minor gap is the lack of tools for creating or modifying documents, but this is reasonable for a read-focused server, and agents can work around this with the update_documentation tool for broader changes.
Average 3.2/5 across 5 of 5 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
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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 mentions 'retrieves' and 'parsed content', implying a read-only operation, but doesn't specify details like whether it requires authentication, handles errors (e.g., invalid paths), returns structured data, or has rate limits. This leaves significant gaps for a tool with no annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized with two sentences that are front-loaded and waste-free. The first sentence states the core purpose, and the second provides essential parameter context, making it efficient and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (a read operation with one parameter), lack of annotations, and no output schema, the description is incomplete. It doesn't explain what 'parsed content' entails (e.g., text, structured data), error handling, or how it differs from sibling tools, leaving the agent with insufficient context for reliable use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds meaning to the single parameter 'path' by specifying it should be 'relative to the Pydantic-AI documentation root's 'docs' directory' and gives an example ('history/0.1.0.md'), which is helpful since schema description coverage is 0%. However, it doesn't fully compensate by detailing constraints like allowed file formats or path validation rules, keeping it at a baseline level.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'retrieves' and the resource 'parsed content of a specific changelog file', making the purpose understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_document_by_path' or 'list_available_changelogs', which could handle similar content or listing functions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides some context about the path format ('relative to the Pydantic-AI documentation root's 'docs' directory'), but offers no guidance on when to use this tool versus alternatives like 'get_document_by_path' for general documents or 'list_available_changelogs' for listing changelogs. There's no explicit when/when-not or alternative tool recommendations.
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 mentions cloning/updating and rebuilding, which implies mutation operations, but doesn't specify whether this is destructive, requires authentication, has side effects on existing documentation, or involves rate limits. The description lacks critical behavioral context for a tool that modifies repository content and search indexes.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise at just one sentence that efficiently describes the tool's three main operations. Every word earns its place, with no redundant information. The structure is front-loaded with the core functionality, making it easy for an agent to quickly understand what the tool does.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (involving repository operations, parsing, and index rebuilding) with no annotations, no output schema, and minimal parameter documentation, the description is inadequate. It doesn't explain what 'updates' means versus 'clones', what format the search index takes, whether the operation is idempotent, or what happens on failure. For a mutation tool with infrastructure impact, this leaves too many gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description doesn't mention the 'force_clone' parameter at all, and with 0% schema description coverage, the parameter remains undocumented in both schema and description. However, since there's only one parameter, the baseline is higher than for multi-parameter tools. The description's mention of 'clones/updates' provides some implicit context for what 'force_clone' might control, but this is insufficient for clear parameter understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('clones/updates', 'parses', 'rebuilds') and identifies the target resource ('Pydantic repo', 'docs', 'search index'). It distinguishes itself from sibling tools that focus on retrieving content rather than updating infrastructure. However, it doesn't fully differentiate from potential non-sibling tools that might perform similar operations on different repositories.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does 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. It doesn't mention prerequisites (e.g., needing repository access), frequency considerations, or when the 'force_clone' parameter should be used. With sibling tools focused on content retrieval, there's no explicit comparison to help an agent decide between update operations and read operations.
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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool lists files but doesn't describe return format, pagination, error handling, or any constraints (e.g., rate limits, authentication needs). This leaves significant gaps for a tool with no structured behavioral hints.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core action. It avoids unnecessary words, though it could be slightly more structured (e.g., by explicitly noting it returns a list of file names).
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations, no output schema, and a simple but undocumented behavioral profile, the description is incomplete. It doesn't explain what the output looks like (e.g., list of strings, JSON structure), error conditions, or any operational context, making it inadequate for reliable agent use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has 0 parameters, and the input schema has 100% description coverage (though empty). The description adds no parameter information, which is appropriate here. Baseline is 4 for zero parameters, as there's nothing to compensate for.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Lists') and resource ('all available changelog files'), and identifies the location ('Pydantic-AI documentation repository'). It distinguishes from some siblings like 'get_changelog_content' (which retrieves content) but doesn't explicitly differentiate from 'list_topics' or 'get_document_by_path'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does 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. It doesn't mention prerequisites, context for usage, or comparisons to sibling tools like 'list_topics' or 'get_document_by_path', leaving the agent to infer usage based on tool names alone.
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?
No annotations are provided, so the description carries full burden. It discloses the tool's scope ('non-recursively') and path context, but lacks critical behavioral details such as return format (e.g., list of strings, objects with metadata), pagination, error handling, or authentication requirements. For a tool with no annotation coverage, this leaves significant gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise with two sentences that directly convey essential information: the action, scope, and path context. Every word serves a purpose, and it's front-loaded with the core functionality, making it efficient and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations, no output schema, and low schema coverage, the description is incomplete. It covers basic purpose and parameter context but omits critical details like return values, error conditions, and behavioral constraints. For a tool that likely returns a list of items, the lack of output information is a significant gap.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, but the description adds meaningful context for the single parameter: it explains that 'path' is relative to 'docs/' directory and clarifies the tool's scope. However, it doesn't detail parameter format (e.g., string patterns, null behavior) or provide examples, leaving some ambiguity despite compensating partially for the schema gap.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('Lists files and directories') and resources ('within the Pydantic documentation'), and specifies the scope ('non-recursively'). It distinguishes from potential recursive listing tools but doesn't explicitly differentiate from sibling tools like 'list_available_changelogs' or 'get_document_by_path'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context by specifying 'non-recursively' and the path's relation to 'docs/' directory, suggesting this is for browsing documentation structure. However, it provides no explicit guidance on when to use this tool versus alternatives like 'get_document_by_path' or 'list_available_changelogs', nor does it mention prerequisites or exclusions.
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 provided, the description carries the full burden. It discloses key behavioral traits: it's a read operation ('Retrieves'), specifies the return type ('ParsedDocument if found, otherwise None'), and mentions the path format ('relative to the Pydantic documentation root'). However, it doesn't cover error handling, performance, or authentication needs.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with zero waste: the first defines the tool's purpose and parameter, the second specifies the return behavior. It's front-loaded with the core functionality and appropriately sized for a simple retrieval tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (1 parameter, no output schema, no annotations), the description is largely complete: it covers purpose, parameter semantics, and return values. However, it lacks details on error cases beyond 'None' and doesn't mention if there are rate limits or permissions required.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It adds meaning by explaining the 'path' parameter as 'relative to the Pydantic documentation root' and provides an example ('usage/models.md'), which clarifies the expected format beyond the schema's basic string type.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Retrieves'), resource ('a specific document'), and mechanism ('by its path relative to the Pydantic documentation root'). It distinguishes from siblings by focusing on path-based retrieval rather than listing topics/changelogs or updating documentation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when you need a document by its path (e.g., 'usage/models.md'), but provides no explicit guidance on when to use this versus alternatives like list_topics or update_documentation. The context is clear but lacks sibling differentiation or exclusions.
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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