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Glama

Obis

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?

Annotations already indicate read-only, idempotent, open-world, and non-destructive. The description adds critical behavioral detail beyond annotations: the distinction between could_not_verify (failure, not evidence) and unsupported (no source), the tolerance_pct override behavior, and the citation requirement. This is essential for correct interpretation of results.

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 carries unique value: examples, routing logic, verdict list, caller warning, and efficiency note. It is well-structured and front-loaded with the most important use-case information. No fluff.

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 complex tool with two pipelines, multiple verdicts, and an error state, the description covers all necessary aspects: when to use, what it returns, how to interpret could_not_verify, and parameter semantics. Even without an output schema, the verdict list and citation explanation make the behavior predictable.

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%, but the description enriches both parameters. For claim, it gives concrete examples. For tolerance_pct, it explains how it overrides the claim's implied tolerance and suggests values for hallucination detection. This goes far beyond the schema's property descriptions.

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. It provides multiple example phrasings and distinguishes itself by returning a structured verdict, making it distinct from general-purpose query 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 Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states when to use: 'Use whenever the agent needs to check whether something a user said is factually correct.' It also describes the two routing paths (company-financial vs. other claims), giving clear context for expected behavior. No alternatives are named, but the trigger conditions are unambiguous.

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

A3.7/5.0
Disambiguation2/5

Several tool clusters have unclear boundaries, most notably ask_pipeworx / ask_pipeworx_beta (which currently matches ask_pipeworx exactly) / ask_pipeworx_grounded, as well as bet_research and the five polymarket_* tools which all surface betting opportunities. ai_visibility_check and scan_competitor_ai_presence overlap, and entity_profile/compare_entities/resolve_entity share inputs. Extensive descriptions help differentiate, but an agent could easily misselect between near-duplicate entry points.

Naming Consistency4/5

Names are uniformly snake_case and mostly follow verb_noun or prefix-group patterns such as ask_pipeworx_*, polymarket_*, remember/recall/forget, and subscribe/unsubscribe. Minor deviations like entity_profile and recent_changes drop the verb, but the overall convention is predictable and readable.

Tool Count2/5

34 tools is beyond the 25+ threshold and the server bundles many unrelated domains—Pipeworx data research, Polymarket analysis, memory, subscriptions, AI visibility, npm scanning, and OBIS marine data. Many tools are redundant variants (six Polymarket tools, four ask_pipeworx variants) that inflate the surface area without adding distinct capabilities.

Completeness3/5

The data-query and prediction-market domains are thoroughly covered: routing, grounded answers, deep research, claim validation, entity comparison, edge detection, fill risk, and subscriptions. However, the server's namesake OBIS surface is skeletal—only occurrence samples, taxon resolution, and aggregate statistics, with no full-record access or dataset browsing—and the broad research purpose leaves notable raw-dataset access hidden behind the meta-router.

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