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Glama

Census

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. Added

TDQS

A4.6/5.0
Behavior5/5

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

Beyond the annotations, the description discloses critical behavioral nuances: could_not_verify means the check did not happen and is not evidence, while unsupported means no source covers it. It also details the returned verdict, actual value with citation, and reasoning, which are not obvious from the schema or annotations.

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 well-structured and front-loaded with the purpose and usage, then covers routing, return values, and error semantics. It is somewhat long due to the list of invocation phrases and the 'Replaces 4–6 sequential calls' note, but each section provides meaningful context.

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?

For a 2-param tool with no output schema, the description fully covers input, routing logic, return value structure (verdict, actual value, citation, reasoning), and edge cases (could_not_verify vs unsupported). This is sufficient for an agent to invoke it correctly and interpret results.

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?

Both parameters are fully described in the schema (100% coverage), so the baseline is 3. The description adds value by explaining tolerance_pct's override behavior, default (implied by wording, capped at 5), and specific use for hallucination detection, which goes beyond the schema's basic definition.

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 states the tool performs natural-language claim verification against authoritative sources, using a specific verb and resource. It clearly differentiates between the SEC EDGAR/XBRL fast path for company-financial claims and the grounded pipeline for other claims, distinguishing it from sibling tools.

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 explicitly says 'Use whenever the agent needs to check whether something a user said is factually correct' and explains the routing between financial and non-financial claims. However, it does not name alternative tools or state when not to use it, so it lacks full when-not/alternatives guidance.

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

A3.8/5.0
Disambiguation2/5

Multiple tools appear to do the same thing: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all provide natural-language data lookup, with ask_pipeworx_beta explicitly identical to ask_pipeworx. The six Polymarket tools also have heavily overlapping boundaries, and the Census-specific tools overlap with each other and with ask_pipeworx's routing to Census data.

Naming Consistency3/5

All names are lowercase snake_case, which is consistent, but the pattern is a mix: some are verb_noun (discover_tools, generate_llms_txt, list_subscriptions) while many are noun_verb or bare noun phrases (entity_profile, pipeworx_feedback, polymarket_edges, census_acs). Suffixes like _beta, _grounded, and the scattered noun-first names prevent a uniform convention.

Tool Count2/5

36 tools is well into the too-heavy range for a coherent server. Even if the domain truly is broad data research and prediction markets, many of these tools are near-duplicates or serve the same purpose with minor variations, so the count feels inflated rather than scoped.

Completeness4/5

The broad data-research domain is well covered: single lookups (ask_pipeworx), grounded verification (ask_pipeworx_grounded, validate_claim), multi-source research (deep_research, entitity_profile, compare_entities, recent_changes), plus memory and subscription utilities. Minor gaps exist (e.g., no general data update/delete, but data is read-only; no direct Census variable explorer beyond census_available_datasets), but no critical dead ends appear.

Resources