Skip to main content
Glama

Data Centrevaldeloire

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

A4.5/5.0
Behavior5/5

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

The description goes far beyond the readOnly/idempotent annotations by detailing the dual-path internal routing (SEC EDGAR+XBRL vs grounded pipeline), explaining the meaning of each verdict, and issuing a critical warning that 'could_not_verify' must not be treated as evidence. This is rich behavioral context that structured annotations alone cannot provide.

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 dense but every sentence contributes: trigger phrases, routing logic, verdict definitions, error semantics, and performance benefit. It is front-loaded with examples and the important caller warning, making it well-structured despite its length.

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?

With no output schema, the description fully specifies the returned verdict set, the payload (grounded/structured actual value, citation, reasoning), and the critical error semantics. It explains both fast-path and fallback behaviors, giving callers a complete contract of what the tool will do.

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?

Both parameters are fully described in the schema (100% coverage), so the baseline is 3. The description adds minimal parameter-specific insight beyond mentioning 'exact percent-delta math' and the tolerance override behavior, but the schema already explains tolerance_pct and its default behavior adequately.

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 explicit trigger phrases ('fact check', 'verify the claim that…') and clearly defines the tool's function as natural-language claim verification against authoritative sources. It stands out from siblings by specifying the verdict output and noting it replaces 4–6 sequential calls, making it the definitive tool for this task.

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 states 'Use whenever the agent needs to check whether something a user said is factually correct' and distinguishes the handling of company-financial claims vs. other claims. It doesn't name specific alternative tools or exclusion scenarios, but the usage context is unambiguous and the routing guidance is clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Data Centrevaldeloire toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.9/5.0
Disambiguation2/5

Several tools have overlapping boundaries, especially ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and validate_claim, all of which route through the same underlying source catalog. The beta variant is explicitly identical to the stable router right now, which forces agents to pick between tools that currently do the same thing. Many of the polymarket tools are also close enough that an agent must read long descriptions carefully to avoid mis-selection.

Naming Consistency3/5

The set is readable and mostly snake_case, but the naming conventions are mixed: some tools use verb_noun (search_datasets, compare_entities, validate_claim), some are noun-like (entity_profile, dataset_info, bet_research), and some use product prefixes (pipeworx_trending, polymarket_edges). There is no single consistent pattern, though related clusters are internally recognizable.

Tool Count2/5

Thirty-four tools is above the heavy threshold, and the apparent server purpose from the name is a regional open-data portal, which only needs the three dataset tools. The remaining tools are a sprawling Pipeworx research assistant covering prediction markets, npm dependencies, AI visibility, memory, subscriptions, and feedback, making the set feel overstuffed and poorly scoped for the stated server.

Completeness4/5

Within the broad data/research scope the coverage is quite deep: discovery, querying, grounded verification, entity profiling, comparisons, monitoring, subscriptions, memory, and prediction-market analysis all have dedicated tools. The Centre-Val de Loire core is adequately covered by search_datasets, dataset_info, and query, though a raw download or full-catalog listing endpoint is missing. The real weakness is not missing lifecycle steps but unclear boundaries between overlapping meta-tools.

Resources