Skip to main content
Glama
Program-Integrity-Alliance

PIA MCP Server

Official
by Program-Integrity-Alliance
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation4/5

    The two tools have distinct focal areas: pia_search provides a general search across all PIA data sources, while pia_oversight_recommendations targets the Open Recommendations dataset specifically. However, since pia_search also includes recommendations, an agent could be uncertain which to use for a recommendation-focused query, though the detailed descriptions mitigate this.

    Naming Consistency4/5

    Both tools share the 'pia_' prefix, which provides a consistent namespace, but one uses a verb ('search') and the other a noun phrase ('oversight_recommendations'), creating a slight structural inconsistency. With only two tools, the pattern is not fully established, but the names are descriptive and readable.

    Tool Count3/5

    Two tools is on the thin side for a server covering multiple government data sources and document types, but both tools are highly parameterized and comprehensive in scope, so it is borderline rather than severely under-provisioned. The count feels reasonable if the server's purpose is narrowly focused on searching.

    Completeness4/5

    The server covers the search domain well, with one tool for broad document search and one for the specialized Open Recommendations dataset, including facets and filter discovery. Gaps include lack of a dedicated tool for retrieving individual documents or covering datasets other than recommendations, but these are workable via pia_search.

  • Average 4.4/5 across 2 of 2 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 13 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing

  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

  • Add a glama.json file to provide metadata about your server.

  • If you are the author, simply .

    If the server belongs to an organization, first add glama.json to the root of your repository:

    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}

    Then . Browse examples.

  • Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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

  • Behavior5/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    With no annotations provided, the description carries the full burden and delivers richly: it discloses that SourceDocumentDataSource filters for non-GAO/Oversight.gov sources are ignored, that free-text counts are approximate while pure filter/agency lookups are exact, and that results are a representative sample with no per-document citations, requiring the govquery_url for the full set.

    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 fairly long (~400 words) but front-loaded with the core purpose as the first clause. Most sentences earn their place given the tool's complexity, though the referenced_agencies warning in the description partially duplicates what the filter parameter schema already states.

    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 complex tool with 8 parameters, an output schema, and tricky usage rules, the description is remarkably complete: it defines total_count semantics, explains results/facets behavior, caveats approximate versus exact counts, and mandates the govquery_url citation rule. No significant gaps remain.

    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?

    All 8 parameters already have schema descriptions (100% coverage), so the baseline is 3. The description adds some value by explaining the GAO/Oversight.gov source restriction and reinforcing the query-versus-referenced_agencies interaction, but most parameter semantics are already covered in 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 opens with 'Search oversight recommendations (Open Recommendations dataset)' — a specific verb+resource with clearly defined scope. It distinguishes this tool from the general sibling pia_search by noting it automatically targets the Open Recommendations dataset and that SourceDocumentDataSet filtering is unnecessary.

    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 provides explicit do/don't guidance: 'Do NOT filter by SourceDocumentDataSet', 'When a text query is provided, do NOT add a referenced_agencies filter', and 'Only use referenced_agencies when the query is empty'. However, it never names the sibling pia_search as an alternative for other use cases, so the when-to-use-vs-alternatives message is incomplete.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior5/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    With no annotations, the description carries full behavioral disclosure. It explicitly explains that counts in content mode are text chunks, not documents, that counts are approximate for free-text queries, and it mandates citation and References formatting plus a Find Out More section. This is rich, actionable transparency 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.

    Conciseness4/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is longer than average but well-structured with labeled sections (WHAT THE COUNTS MEAN) and front-loaded purpose. Each sentence adds significant guidance for a complex tool, though some redundancy with schema descriptions could be trimmed.

    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 the tool's complexity (9 parameters, output schema present), the description adds essential context: count semantics, citation requirements, wide-mode behavior, and facet discovery. It is complete enough for an agent to invoke the tool correctly and interpret results accurately.

    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 parameters are already well documented. The description adds clarifying context around count interpretation and the difference between content vs titles modes, but much of this is also present in the schema's parameter descriptions. It adds marginal value without fully compensating for any gaps.

    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 tool searches the PIA database of government oversight reports and related documents, with a specific verb ('Search') and resource ('PIA database'). It distinguishes itself as 'One tool for all document search' and covers scoping modes, which separates it from the sibling tool pia_oversight_recommendations.

    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 gives clear guidance on when to use specific modes, such as using search_mode='titles' when users ask for document counts, and identifies wide/facets_only as specialized uses. It does not explicitly mention when to avoid this tool or name the sibling as an alternative, so it stops shy of full when/when-not guidance.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

GitHub Badge

Glama performs regular codebase and documentation scans to:

  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

pia-mcp-local MCP server

Copy to your README.md:

Score Badge

pia-mcp-local MCP server

Copy to your README.md:

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/Program-Integrity-Alliance/pia-mcp-local'

If you have feedback or need assistance with the MCP directory API, please join our Discord server