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Jaldekoa

MCP-FREDAPI

by Jaldekoa
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool has a clear and distinct purpose, making disambiguation trivial.

    Naming Consistency5/5

    Since there is only one tool, naming consistency is inherently perfect. The tool name follows a clear verb_noun pattern (get_fred_series_observations), which would be consistent if more tools existed.

    Tool Count2/5

    A single tool is too few for a server named MCP-FREDAPI, which suggests broader functionality related to the Fred API. This minimal toolset likely leaves significant gaps in coverage for typical API interactions.

    Completeness1/5

    The server is severely incomplete, offering only one tool to get series observations. It lacks essential operations like listing series, searching, or managing data, which are core to interacting with the Fred API domain.

  • Average 2.7/5 across 1 of 1 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

  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

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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?

    No annotations are provided, so the description carries the full burden of behavioral disclosure. It states 'Get series observations' which implies a read-only operation, but doesn't mention any behavioral traits like rate limits, authentication requirements, error handling, or what the return format looks like (e.g., JSON structure, pagination). This leaves significant gaps for an API tool.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness5/5

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

    The description is a single, efficient sentence with zero waste—it directly states the tool's function without unnecessary words. It's appropriately sized for a tool with a clear name and detailed schema, though it could benefit from more context.

    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 (13 parameters, no output schema, no annotations), the description is incomplete. It doesn't explain the output format, error conditions, or typical usage patterns. For a data retrieval tool with many filtering options, more guidance on what 'observations' entail and how results are structured 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%, meaning all parameters are well-documented in the schema itself. The description adds no additional meaning beyond the schema—it doesn't explain parameter interactions, provide examples, or clarify complex concepts like 'realtime period' vs. 'observation period'. Baseline 3 is appropriate when the schema does the heavy lifting.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose3/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description 'Get series observations from the Fred API' states the basic action (get) and resource (series observations), but it's vague about what 'observations' means in this context (e.g., economic data points over time). With no sibling tools, it doesn't need differentiation, but the purpose lacks specificity about the nature of the data being retrieved.

    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, prerequisites, or typical use cases. With no sibling tools, it doesn't need sibling differentiation, but it offers no context about appropriate scenarios (e.g., retrieving historical economic indicators).

    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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Glama performs regular codebase and documentation scans to:

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  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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