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Glama

Fcc Ecfs

by io.github.pipeworx-io

Validate Claim

validate_claim
Read-onlyIdempotent

"Is it true that…" / "fact check" / "verify the claim that…" / "did X really…" / "was Y actually…" / "confirm or refute" / "true or false" — natural-language claim verification against authoritative sources. Use whenever the agent needs to check whether something a user said is factually correct. Company-financial claims (revenue, net income, cash for public US companies) verify via the structured SEC EDGAR + XBRL fast path with exact percent-delta math; ANY OTHER factual claim (macro statistics, rates, prices, drug data, records) automatically falls through to the grounded pipeline — routed to the right live source, answered with verbatim evidence, then judged. Returns a verdict (confirmed / approximately_correct / refuted / inconclusive / unsupported / could_not_verify), the grounded or structured actual value with pipeworx:// citation, and reasoning. IMPORTANT for callers: could_not_verify means the check did not happen (our LLM or source failed) and carries verification_error{stage,detail} — it is NOT evidence for or against the claim, and must not be shown as one. unsupported means we looked and cover no source for it. Replaces 4–6 sequential calls (NL parsing → entity resolution → data lookup → comparison).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
claimYesNatural-language factual claim, e.g., "Apple's FY2024 revenue was $400 billion" or "Microsoft made about $100B in profit last year".
tolerance_pctNoMax percent deviation still graded approximately_correct (0.5–50). Overrides the tolerance implied by the claim wording — set 1–2 for hallucination detection where any material error must be refuted. Default: implied by wording, capped at 5.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A5/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description goes beyond these by explaining the meaning of two verdicts ('could_not_verify' indicates a pipeline failure with verification_error; 'unsupported' means the source coverage was searched and absent), which is critical for correct agent behavior. It also details the dual routing (SEC EDGAR vs grounded pipeline) and the return structure, providing rich behavioral context not present in annotations.

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 yet information-dense. It front-loads purpose with examples, explains the two routing paths, lists verdicts, gives an important caller caveat, and concludes with efficiency benefit. Every sentence adds value; there is no redundancy or filler.

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 (two processing paths, six verdict types, error semantics) and the absence of an output schema, the description is remarkably complete. It covers input examples, behavior by claim category, output structure (verdict, grounded/structured value, citation, reasoning), and failure-mode distinctions, enabling the agent to invoke the tool correctly and interpret results safely.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema descriptions for both parameters are thorough, and the description adds further meaning, especially for tolerance_pct: it specifies the allowed range (0.5–50), how it overrides the wording-derived default, and a use case (set 1–2 for hallucination detection). This directly adds actionable guidance beyond the schema, which already has 100% 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 opens with natural-language query examples ('fact check', 'verify the claim that…') followed by a clear statement of purpose: 'natural-language claim verification against authoritative sources.' It explicitly distinguishes this from siblings by focusing on yes/no claim verification and describes its two processing paths (SEC EDGAR fast path and grounded pipeline), making it unmistakable what the tool does.

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 tells when to use: 'Use whenever the agent needs to check whether something a user said is factually correct.' It also provides a rule for when not to interpret a result as evidence ('could_not_verify means... must not be shown as one') and positions the tool as a replacement for 4–6 sequential calls, inherently guiding the agent to prefer this over multi-step alternatives.

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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TDQS

A3.6/5.0
Disambiguation2/5

Many tools overlap in purpose, such as multiple query tools (ask_pipeworx, ask_pipeworx_grounded, deep_research) and multiple prediction market analysis tools (bet_research, polymarket_arbitrage, polymarket_edges). An agent would struggle to select the appropriate tool due to ambiguous distinctions.

Naming Consistency3/5

Tool names are a mix of styles: some follow verb_noun (ask_pipeworx, forget), others use noun_verb (ecfs_docket_filings), and many are short or compound (bet_research, entity_profile). While the ecfs- prefix tools are consistent, the overall set lacks a uniform pattern.

Tool Count2/5

With 35 tools, the count is excessive for a server named after FCC ECFS, as only 4 tools are directly relevant to that domain. The inclusion of many general-purpose data tools makes the set feel bloated and misaligned with the server's apparent scope.

Completeness3/5

For the FCC ECFS domain, the tool set is incomplete (only 4 tools, lacking submission or deletion capabilities). However, as a general-purpose data query server, it covers many sources (SEC, FDA, patents, etc.). The name mismatch hurts the perceived completeness.

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