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Glama

Zenodo

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

A4.4/5.0
Behavior5/5

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

The description adds substantial behavioral context beyond the annotations: it clarifies the critical distinction between could_not_verify (check did not happen, not evidence) and unsupported (no source found), explains the return payload (verdict, value, citation, reasoning), and discloses two distinct processing pipelines. This is far beyond what annotations provide, with no contradictions.

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 long but well-structured: it leads with natural-language trigger phrases, then purposes, process, return values, and important caller warnings. The length is justified by the tool's complexity, but some sentences (e.g., 'Replaces 4–6 sequential calls') are somewhat promotional and could be trimmed without losing critical information.

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?

This is a complex tool with no output schema, so the description must cover return values and semantics. It does: all six verdicts are listed, could_not_verify vs unsupported is explained, the grounded vs structured path is described, and the actual value with citation is mentioned. This is sufficient for an agent to correctly interpret results.

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?

Schema description coverage is 100%: both claim and tolerance_pct have detailed schema descriptions, including examples and edge-case guidance. The main description does not add significant parameter-specific information beyond what the schema already covers. Per the baseline, a score of 3 is appropriate when the schema carries the load.

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 identifies the tool as a natural-language claim verification tool, using strong verbs like 'fact check', 'verify', 'confirm or refute'. It distinguishes itself by specifying the dual-path approach (SEC EDGAR for financial claims, grounded pipeline for others), which differentiates it from generic search/ask siblings.

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 when to use the tool: 'Use whenever the agent needs to check whether something a user said is factually correct.' It also explains the two internal routing paths and implies efficiency by saying it replaces 4-6 sequential calls. However, it does not name alternative sibling tools to consider, so the exclusion guidance is implicit rather than explicit.

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

A3.5/5.0
Disambiguation2/5

Multiple tools have unclear boundaries: ask_pipeworx and ask_pipeworx_beta are explicitly identical right now, while ask_pipeworx, ask_pipeworx_grounded, deep_research, discover_tools, and suggest_questions all overlap in the 'how should I query data' space. Additionally, the tool set mixes two unrelated domains (Zenodo and Pipeworx) without any organizing principle, forcing agents to guess which family applies.

Naming Consistency2/5

All names are lowercase snake_case, but the semantic patterns are inconsistent: bare nouns for Zenodo tools (search, record, communities), product-prefixed names (pipeworx_*, polymarket_*), generic verbs (remember, forget, recall), and verb_noun compounds (list_subscriptions, generate_llms_txt). The server is named Zenodo, yet most tools carry a pipeworx or polymarket prefix, making the naming feel arbitrary relative to the server's identity.

Tool Count2/5

36 tools is excessive for a server whose stated identity is Zenodo; only 5 of the 36 tools actually relate to Zenodo, with the remaining 31 belonging to a separate Pipeworx data platform. This suggests a bundled or mislabeled server rather than a deliberately scoped tool surface, and even the Zenodo subset alone would be thin.

Completeness2/5

For the Zenodo domain implied by the server name, the surface is severely incomplete: it covers search and read/retrieval (search, record, record_files, communities, community_records) but entirely omits the deposit workflow that is central to Zenodo — no create, update, delete, versioning, or file upload/download tools. The unrelated Pipeworx side is over-built, but the actual Zenodo use case leaves agents with dead ends.

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