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kablewy

FRED MCP Server

by kablewy
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TypeScript
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 purposes: 'search' is for finding data series, while 'series' is for retrieving observations for a specific series. There is minimal overlap, though the 'advanced options' in both descriptions could cause slight confusion if not detailed further, but the core functions are clearly separated.

    Naming Consistency5/5

    Both tool names are single, lowercase nouns ('search' and 'series'), which is consistent and simple. There are no mixed conventions or deviations, making the naming pattern predictable and easy to understand.

    Tool Count2/5

    With only 2 tools, the server feels thin for a data service like FRED, which typically involves more operations such as listing categories, getting metadata, or managing favorites. This limited set may hinder agents from performing comprehensive tasks in the domain.

    Completeness2/5

    The tool surface is severely incomplete for a FRED server. It lacks essential operations like listing available series categories, retrieving series metadata, or supporting updates and deletions. Agents will face significant gaps when trying to navigate or manipulate FRED data beyond basic search and observation retrieval.

  • Average 3/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 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 AGPL 3.0.

  • This repository includes a README.md file.

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

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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 full burden for behavioral disclosure. It states this is a 'Get' operation (implying read-only), but doesn't mention authentication requirements, rate limits, error conditions, pagination behavior (beyond the limit/offset parameters), or what the output looks like. For a tool with 10 parameters and no output schema, this leaves significant behavioral 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 a single, efficient sentence that clearly states the core purpose. Every word earns its place - 'Get observations' establishes the action, 'for a specific FRED data series' specifies the resource, and 'with advanced options' hints at the parameter complexity without unnecessary elaboration.

    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?

    For a tool with 10 parameters, no annotations, and no output schema, the description is insufficiently complete. It doesn't explain what 'observations' are in the FRED context, doesn't mention authentication requirements, doesn't describe the return format, and provides no guidance on parameter interactions. The 100% schema coverage helps, but the description itself lacks necessary context for effective tool selection and 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?

    Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds minimal value beyond what's in the schema - it mentions 'advanced options' which hints at the numerous parameters, but doesn't provide additional context about parameter interactions, defaults, or usage patterns. Baseline 3 is appropriate when schema does the heavy lifting.

    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: 'Get observations for a specific FRED data series with advanced options'. It specifies the verb ('Get'), resource ('observations for a specific FRED data series'), and scope ('with advanced options'). However, it doesn't explicitly differentiate from the sibling 'search' tool, which likely searches for series rather than retrieving observations for a specific series.

    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 mentions 'advanced options' but doesn't specify what makes it advanced or when simpler alternatives might exist. There's no mention of the sibling 'search' tool, prerequisites, or typical use cases.

    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 'advanced filtering options' but doesn't explain what 'advanced' entails, such as pagination, rate limits, authentication needs, or what happens with invalid inputs. For a search tool with 8 parameters, this leaves significant gaps in understanding its behavior.

    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 that front-loads the core purpose without unnecessary words. It's appropriately sized for a search tool, making it easy to parse quickly.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness3/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given the complexity (8 parameters, no annotations, no output schema), the description is minimal. It states the purpose but lacks details on usage, behavioral traits, or output format. While concise, it doesn't fully compensate for the missing structured data, leaving room for improvement in completeness.

    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 schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds minimal value by hinting at 'advanced filtering options,' but doesn't provide additional meaning or context beyond what's in the schema. 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.

    Purpose4/5

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

    The description clearly states the verb ('Search') and resource ('FRED data series'), and mentions 'advanced filtering options' which adds specificity. However, it doesn't explicitly differentiate from the 'series' sibling tool, which might offer different functionality like retrieving specific series details rather than searching.

    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 the 'series' sibling tool, nor does it mention any prerequisites, contexts, or exclusions for usage. It's a generic statement that lacks operational direction.

    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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