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Data Montgomery

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

A5/5.0
Behavior5/5

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

Beyond the readOnly/idempotent/openWorld annotations, the description discloses critical behavior: the two-path execution (SEC EDGAR vs grounded pipeline), the meaning of each verdict type, and the crucial caveat that could_not_verify is not evidence. It also states the tool verifies with verbatim evidence and citations, adding substantial 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 front-loaded with trigger phrases and a clear purpose, then structured into routing behavior, return values, and caller warnings. Every sentence provides necessary operational detail—no filler. The 'IMPORTANT for callers' section is efficiently integrated.

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?

Without an output schema, the description fully compensates by enumerating verdict types, explaining the difference between could_not_verify and unsupported, and mentioning the citation and reasoning included in results. It also covers the tool's role in replacing multi-step pipelines, making it self-sufficient for an agent.

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?

Schema coverage is 100% and both parameters are well-described. The description adds extra meaning for tolerance_pct by explaining that it overrides the default implied by wording and giving a specific use case (hallucination detection with 1-2%). Claim receives concrete examples, reinforcing 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?

The description clearly states the tool's purpose: natural-language claim verification against authoritative sources, with trigger phrases like 'fact check' and 'verify the claim that'. It distinguishes itself from siblings by highlighting the SEC EDGAR fast path for company-financial claims and fallback to the grounded pipeline, and by explicitly noting it replaces 4-6 sequential calls.

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?

The description explicitly says 'Use whenever the agent needs to check whether something a user said is factually correct' and clarifies the routing behavior for company-financial vs other claims. It also explains important callers' semantics for could_not_verify and unsupported, guiding when to trust the result and when not to.

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

A4.1/5.0
Disambiguation3/5

Several tools have overlapping purposes, such as ask_pipeworx, ask_pipeworx_grounded, and deep_research, which may confuse an agent about which to use for a given query. Additionally, the many prediction market tools (polymarket_arbitrage, polymarket_edges, etc.) have subtle distinctions that could lead to misselection.

Naming Consistency4/5

Tool names are predominantly lowercase with underscores and follow a descriptive pattern (e.g., compare_entities, resolve_entity, scan_dependency). There are minor deviations like bet_research vs. research-related tools, but the overall pattern is consistent.

Tool Count3/5

With 33 tools, the server covers many domains (data lookup, prediction markets, Montgomery County data, npm scanning, etc.), making it feel heavy. While each tool has a clear purpose, the broad scope borders on excessive for a single server.

Completeness3/5

The server provides a wide range of data access and analysis tools, but it lacks basic CRUD operations for its data sources (e.g., no way to create or update records in Montgomery County data or Pipeworx). Some domain coverage is incomplete (e.g., no tool for listing all Pipeworx tools, only discover_tools with top-N results).

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