cfr-compliance-mcp

by vvXranjan

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Python

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Server Quality Checklist

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Latest release: v0.1.0
Disambiguation4/5
Most tools are clearly separated by operation type and granularity: retrieve_section/part/title, get_title_structure vs get_version_history, and list_agencies are all distinct. The main area of potential confusion is search_regulations vs search_by_keyword, though the descriptions do explain the free-text vs keyword-list difference.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern: search_*, retrieve_*, get_*, and list_*. The use of retrieve for legal text and get for metadata/history is a predictable and readable convention.
Tool Count5/5
Eight tools is well-scoped for a CFR research server. The set covers search, retrieval at three granularity levels, structural navigation, version history, and agency lookup without redundancy or bloat.
Completeness4/5
The core CFR workflow is well covered: search, navigate structure, retrieve text, and check historical versions. A minor gap is the lack of an explicit list_titles or list_parts tool, but get_title_structure and list_agencies provide workarounds for discovering the CFR hierarchy.
Average 4.3/5 across 8 of 8 tools scored. Lowest: 3.2/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 17 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, the description carries the full burden of behavioral disclosure. It only states that this is a full-text search; it does not mention important behaviors such as date scoping, pagination, agency filtering, or the fact that date=None may return duplicates and superseded text. No contradictions exist, but transparency is minimal.
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 a single, front-loaded sentence with no filler. It communicates the essential operation immediately and every word earns its place, making it a model of concise structure.
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 rich input schema (100% parameter coverage) and the presence of an output schema, the description does not need to explain return values or parameter details. The main gap is that it does not position this tool relative to search_by_keyword, but invocation-critical details are available in the structured data.
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 100%, so the baseline is 3. The description itself adds no parameter-specific meaning beyond the schema, which already thoroughly documents query, date, page, per_page, and agency_slugs, including the nuanced date sentinel behavior.
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 states a specific verb ('search'), a clear resource ('the CFR'), and a specific input type ('free-text query or phrase'), making the tool's core function immediately understandable. However, it does not distinguish itself from the sibling tool search_by_keyword, so it stops short of a 5.
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 gives no guidance on when to use this tool versus alternatives such as search_by_keyword or the various retrieval tools. There is no mention of scenarios that favor this tool or exclusions, so an agent must infer usage from the name and schema alone.
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 carries the full transparency burden. It discloses the most important behavioral characteristic — it returns only hierarchical structure, not regulation text — which prevents an agent from using this as a text-retrieval tool. 'Fetch' also implies a read operation with no mutation.
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?
One sentence that front-loads the action/resource, includes the critical exclusion (no regulation text), and ends with the practical use case. There is no filler or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description is sufficient for a simple fetch tool: it states what is returned, what is not returned, and why an agent would call it, while the supplied output schema covers return structure. The optional date parameter is already documented in the schema, so no additional detail is needed.
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 100%, with both 'title' and 'date' fully described, so the description need not repeat parameter details. The tool description adds no parameter-level semantics beyond the schema, matching the baseline for full schema coverage.
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 names a specific verb ('Fetch') and resource ('hierarchical structure of a CFR title'), and explicitly excludes regulation text, which distinguishes it from sibling retrieval tools like retrieve_title or retrieve_part. The stated purpose ('planning which part/section to retrieve next') further clarifies its role.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description clearly signals when to use this tool: as a planning/navigation step before retrieving a specific part or section. It does not explicitly name alternatives or give when-not-to-use conditions, but the 'no regulation text' caveat and sibling list make the intended context evident.
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 carries the responsibility for behavioral disclosure. It adds one useful warning—that a part's response can be large—but does not mention other operational traits such as pagination, error behavior, or performance implications.
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 two sentences with the core purpose front-loaded and the cautionary sibling guidance in the second sentence. There is no filler, redundancy, or unnecessary detail.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given full schema parameter coverage and an output schema, the description is complete enough for correct tool selection and invocation. It conveys what the tool retrieves, warns about large responses, and directs the agent to the right alternative.
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 coverage is 100% and each parameter is already described with type, default, and example. The description adds contextual color by calling a part a cluster of sections, but it does not add new parameter-level semantics beyond the schema.
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 names the specific operation ('Retrieve the text'), the resource ('entire CFR part'), and defines a part as a cluster of related sections. It also distinguishes itself from retrieve_section by calling out the exact-section scenario.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
It explicitly instructs the agent to prefer retrieve_section when the clause maps to one exact section, giving a clear routing rule. This is sufficient to choose between the closely related siblings.
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 carries the behavioral burden. 'Retrieve' clearly indicates a non-mutating read operation, and 'exact legal text' tells the agent the output is verbatim statutory language rather than a summary. It does not cover auth requirements, rate limits, or error behavior, but for a simple read tool with an output schema these are secondary.
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?
A single, front-loaded sentence that immediately states the action and object, then offers an illustrative example. There is no fluff, no repetition of schema descriptions, and every word contributes.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a low-complexity retrieval tool, the one-sentence description plus a fully documented input schema and an output schema is complete. The agent knows exactly what the tool returns (exact legal text of one section), what parameters to provide (covered in schema), and what format to expect (covered by output schema). No critical selection or invocation information is missing.
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 schema already fully documents all four parameters (100% coverage), so the baseline is 3. The description adds a concrete example, '40 CFR 261.10', which illustrates how the required title, part, and section parameters map to a standard citation and reinforces the expected string formats for part and section.
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 states the specific verb 'Retrieve' and the exact resource: 'the exact legal text of one CFR section', with a concrete example citation. The phrase 'one section' clearly distinguishes it from siblings like retrieve_part and retrieve_title, which operate on larger units, and from search_by_keyword, which searches rather than retrieves exact text.
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 when to use the tool—whenever a single CFR section's text is needed—but it never explicitly contrasts it with alternatives such as retrieve_part, retrieve_title, or search_regulations. The sibling list hints at the family of tools, yet no when-to-use / when-not-to-use guidance is provided.
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 carries the full burden and does well: 'Fetch' indicates a read operation, and the contrast with retrieve_section/part/title clarifies that this tool returns history rather than point-in-time text. It doesn't mention pagination or result limits, but the presence of an output schema partially mitigates that.
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 compact and well-structured: the core purpose is front-loaded, followed by a concrete use case and clear differentiation from siblings. Every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a 6-parameter read tool with an output schema, the description covers purpose, scoping/filtering options, the primary use case, and how it differs from sibling tools. Nothing essential is missing for correct selection and invocation.
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 coverage is 100%, so the baseline is 3. The description mostly paraphrases the scoping/filtering behavior already present in the schema and adds little new parameter-level semantic detail.
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 uses a specific verb and resource: 'Fetch point-in-time version history for a CFR title.' It clearly distinguishes this tool from sibling text-retrieval tools by stating it returns full change history, not just the text as of a date.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
It explicitly states when to use the tool: to determine which version of a regulation was in effect on a specific date. It also names the relevant alternatives and explains why they are not appropriate for change-history needs.
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?
There are no annotations, so the description carries the full behavioral burden. It discloses a key behavioral trait: certain large titles may be slow or time out. It also implies a read-only operation by saying 'Retrieve the text.' It doesn't cover every possible edge case, but it adds meaningful behavioral context beyond the basic operation.
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 compact: one clear purpose sentence followed by a concise, high-signal warning. Every sentence earns its place, the warning is scannable with 'WARNING:' and the guidance is front-loaded without unnecessary detail.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given that the tool has only two parameters, a rich input schema, and an output schema, the description fully covers what an agent needs: purpose, scope, performance caveats, and sibling alternatives. Nothing critical is missing for correct selection and invocation.
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 100%, and the schema already documents title as 'CFR title number (1-50)' and date with a full default/format description. The tool description does not need to repeat parameter details and does not add meaningful parameter-level semantics, 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.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a clear verb and resource: 'Retrieve the text of an entire CFR title.' The word 'entire' disambiguates it from retrieve_part and retrieve_section, so an agent can immediately recognize the tool's scope and distinguish it from sibling tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The warning explicitly states when not to use this tool and names alternatives: 'Strongly prefer retrieve_part or retrieve_section when you know which part or section is relevant — use this only for small titles or genuinely title-wide questions.' This gives the agent actionable selection criteria rather than leaving it to inference.
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 carries the full burden. It discloses both success and error return shapes, including a retryable flag and nested child agencies. It doesn't mention performance or pagination, but for a zero-parameter listing operation this is a minor omission rather than a critical gap.
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 main purpose is front-loaded in the first sentence, followed by a compact usage scenario and a clear return format section. Every sentence earns its place; there is no filler or repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a no-parameter list tool, the description is complete: it explains the resource, the purpose, the relationship to sibling tools, and the expected success and failure payloads. Nothing an agent needs to use it correctly is missing.
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 zero parameters, so there are no parameter semantics to add. The description doesn't need to compensate for schema gaps, and the baseline 4 is appropriate since the dimension is trivially satisfied.
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 states a clear verb-object pair: 'List all agencies referenced in the CFR' with their title/chapter cross-references. It also differentiates itself from search siblings by framing itself as a mapping step before search_regulations/search_by_keyword.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
It explicitly says when to use the tool: to map an implied regulator to a CFR title before running search_regulations or search_by_keyword. This provides actionable context and names the relevant alternatives, even if it doesn't enumerate all non-use cases.
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, and it does disclose an important behavioral trait: 'Keywords are joined into one query string before searching'. This explains how multiple terms are combined and clarifies the search semantics. It could additionally state that the operation is read-only, but 'search' strongly implies a non-destructive read action.
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 two sentences with no filler. The core purpose and distinguishing behavior are front-loaded, and the alternative tool reference is included in the second sentence. Every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has an output schema, fully documented parameters, and a clear sibling pointer. Given the moderate complexity, the description is sufficient for an agent to select and invoke the tool correctly. It covers what the tool does, how keywords are handled, and which sibling to use for phrase searches.
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 input schema already documents all five parameters comprehensively, so the baseline is 3. The description adds value by explaining that keywords are treated as 'distinct keywords/terms' and are 'joined into one query string', which clarifies the intended interpretation of the `keywords` parameter beyond the schema's generic 'list of search terms'.
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 opens with a specific verb and resource: 'Search the CFR using a list of distinct keywords/terms'. It clearly distinguishes this tool from search_regulations by stating the input mode is a list of distinct keywords rather than a single free-text phrase, so an agent can immediately tell the tools apart.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives explicit selection guidance: use this tool for a list of distinct keywords/terms, and explicitly directs users to `search_regulations` for precise multi-word phrase search. This tells the agent not only when to use this tool, but also when to choose an alternative.
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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