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Data Fcc

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. Changed1 schema field changed
    • addedInput schema / properties / tolerance_pct
      Added value: +{
+  "description": "Max 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.",
+  "type": "number"
+}
  2. First observed

TDQS

A4.3/5.0
Behavior5/5

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

The description adds significant behavioral context beyond the annotations: it explains the return structure (verdict, value, citation, reasoning), clarifies the meaning of could_not_verify and unsupported, and warns callers about misinterpreting could_not_verify. This is exactly the kind of nuance annotations cannot convey, so a 5 is warranted.

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 but well-structured and front-loaded with trigger phrases. It uses bold for the critical caveats and organizes the financial vs. general claim distinction clearly. Every sentence serves a purpose, though it could be tightened slightly without losing meaning.

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 lack of an output schema, the description fully compensates by describing what the tool returns (verdict, value, citation, reasoning) and how to interpret ambiguous outcomes. It addresses edge cases, routing logic, and call-savings benefit, making it highly complete for a complex verification tool.

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?

The input schema already provides descriptions for both parameters (100% coverage), so the baseline is 3. The description adds extra semantics for tolerance_pct (overrides wording-implied tolerance, recommended 1–2 for hallucination detection) and gives concrete claim examples, which is value beyond the schema.

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 identifies the tool's purpose: natural-language claim verification against authoritative sources, with specific trigger phrases. It distinguishes scope (financial vs. other claims) and states it replaces a multi-step pipeline, but does not explicitly contrast with sibling tools like ask_pipeworx or deep_research.

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 states when to use: 'Use whenever the agent needs to check whether something a user said is factually correct.' It also explains the routing logic for financial vs. other claims. However, it does not provide explicit exclusions or name alternative tools for non-claim queries, 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.

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TDQS

A3.9/5.0
Disambiguation3/5

Several tools overlap in purpose: ask_pipeworx and ask_pipeworx_beta are explicitly identical right now, and ask_pipeworx_grounded, deep_research, and validate_claim all query the same underlying data catalog in similar ways. The descriptions provide detailed usage guidance, but the query family and the five-strong Polymarket family still create real misselection risk. Memory, subscription, and FCC tools are clearly distinct.

Naming Consistency4/5

Tool names are consistently snake_case and mostly follow recognizable verb_noun or prefixed-family patterns (ask_pipeworx_*, polymarket_*, pipeworx_*). The main deviations are bare one-word names like datasets, metadata, query, remember, and forget, plus a few noun-first names like entity_profile and polymarket_arbitrage. Overall the naming is readable and predictable, with only minor inconsistencies.

Tool Count2/5

At 34 tools, this server is well past the 25+ threshold and feels overloaded. It bundles a general-purpose data-query gateway, FCC open-data access, Polymarket analytics, AI-visibility scanning, memory, subscriptions, npm dependency checks, and llms.txt generation into one surface. Each functional area is small on its own, but the combined set would be more coherent split into several focused servers.

Completeness4/5

The core data lifecycle is well covered: discovery (suggest_questions, discover_tools), single queries (ask_pipeworx), grounded/evidence-backed answers, deep multi-source research, entity resolution/profiling/comparison, change feeds, and fact-checking all exist. FCC open data has search, schema, and query tools, and subscriptions/memory have full CRUD-style coverage. Minor gaps remain (e.g., no direct pipeworx:// URI fetch tool, no enumeration of all discoverable data sources), but they do not block typical workflows.

Resources