Skip to main content
Glama
stefanoamorelli

Federal Reserve Economic Data (FRED) MCP Server

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.
  • Latest release: v1.2.0

  • Disambiguation5/5

    Each tool serves a distinct purpose: browsing catalog structure, searching for series, and retrieving series data. There is no overlap or ambiguity between browse, search, and get_series.

    Naming Consistency5/5

    All tools follow the consistent pattern fred_<verb>, with descriptive action names: browse, search, get_series. Naming is uniform and predictable.

    Tool Count5/5

    Three tools cover the core FRED workflow of exploring, finding, and retrieving economic data. The count is minimal but well-scoped for the server's purpose, avoiding unnecessary bloat.

    Completeness4/5

    The tool set covers the essential operations: browse the catalog, search for series, and retrieve data. Minor gaps exist such as fetching detailed metadata for a specific series or category, but these are not critical for standard use cases.

  • Average 3.6/5 across 3 of 3 tools scored.

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

    • 2 of 2 community issues answered or closed in the last 6 months
    • 12 commits in the last 12 weeks
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing
  • This repository is licensed under AGPL 3.0.

  • 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

  • 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 disclosing behavior. It simply says 'Retrieve data' without stating side effects, limitations, rate limits, output structure, or error conditions. For a read operation, it's not harmful but inadequately informative about what to expect.

    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?

    Two concise sentences, no redundant phrasing. Every word earns its place, but it is perhaps too sparse (under-specified) rather than genuinely concise—still, it is well-structured and front-loaded with the core purpose.

    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?

    The tool has 11 parameters, several with enums, and no output schema. The description provides no information about return formats, required fields beyond the implied series_id, or how to interpret transformations/frequencies. This is insufficient for an agent to confidently select and use the tool without relying heavily on the schema.

    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 the schema already describes each parameter in detail. The description mentions transformations, frequency, and date ranges, which loosely map to units, frequency, and observation_start/end, but adds no new meaning beyond what the schema provides. The baseline of 3 is appropriate since 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 it retrieves data for a FRED series by its ID, which is a specific verb and resource. It does not explicitly differentiate from siblings (fred_browse, fred_search), but the intent is unambiguous—getting the actual data series rather than browsing or searching.

    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—use this to get series data—but provides no explicit guidance on when to choose this over fred_browse or fred_search, nor any exclusions or prerequisites. The mention of supported options (transformations, frequency, date ranges) gives some context but no decision framework.

    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, the description carries full burden. It states it returns series with IDs, titles, and metadata, but does not disclose pagination behavior, the two search types (full_text vs series_id), or any rate limits. This is a significant gap for a tool with 10 parameters and no output schema.

    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?

    Three concise sentences, all relevant, with the most important information front-loaded. No redundancy or fluff.

    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 10 parameters, no output schema, and no annotations, the description is too sparse. It lacks explanation of search types, pagination usage, ordering behavior, and any practical examples. An agent would need more guidance to use the tool effectively.

    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 adds slight value by mapping 'keywords, tags, or filters' to search_text, tag_names, and filter_variable/filter_value, but does not explain other parameters like order_by or search_type 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?

    Description clearly states it searches for FRED economic data series by keywords, tags, or filters, and returns IDs, titles, and metadata. This distinguishes it from sibling tools fred_browse and fred_get_series, as it's search-oriented rather than browsing or direct retrieval.

    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?

    Explicitly advises using this tool when you know what you're looking for, providing clear context for when it applies. However, it does not mention when to use alternatives (fred_browse or fred_get_series) or provide exclusions, so it falls short of a 5.

    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 present, so the description carries the full burden of disclosing behavioral traits. However, it only states that browsing is possible through categories/allows/releases/sources; it does not explicitly state that this is read-only, does not mention pagination behavior, rate limits, return format, or contextual dependencies. Though the word 'browse' implies a read operation, key behavioral expectations remain unstated.

    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 zero fluff. The first sentence gives a high-level summary, and the second defines each browse_type in a compact list. It is front-loaded and necessary information without redundancy.

    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 has 9 parameters, no output schema, and no annotations, the description does a solid job of covering the main polymorphic behavior via browse_type. However, it does not address pagination via limit/offset or how filter parameters relate to actual output, though the schema fills some gaps. It is slightly incomplete for full contextual coverage.

    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 coverage is 100%, giving a baseline of 3. The description adds semantic meaning by explaining exactly what each browse_type enum does (e.g., 'category_series' returns all series in a category), which is not fully embedded in the parameter schema's short string description. This goes beyond the schema and meaningfully guides parameter selection.

    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 (browse) and resource (FRED's complete catalog), and enumerates the distinct browsing modes: categories, releases, sources, category_series, and release_series. This clearly differentiates it from sibling tools like fred_search and fred_get_series by emphasizing catalog exploration vs searching or retrieving a single series.

    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 clear context for using the tool to explore FRED's catalog and details each browse_type variant, but it does not explicitly compare with fred_search or fred_get_series or state when not to use this tool. Since it gives clear operational guidance for internal parameter choices, it meets the 'clear context, no exclusions' bar.

    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

Federal Reserve Economic Data (FRED) MCP Server MCP server – quality and maintenance score on Glama

Copy to your README.md:

Score Badge

Federal Reserve Economic Data (FRED) MCP Server MCP server – quality and maintenance score on Glama

Copy to your README.md: