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Glama

Paperswithcode

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

A4.4/5.0
Behavior5/5

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

With readOnlyHint, openWorldHint, idempotentHint, and destructiveHint annotations already covering safety, the description adds substantial behavioral context: it explains the two-path routing (SEC EDGAR vs. grounded pipeline), details the meaning of 'could_not_verify' vs. 'unsupported' (including the verification_error field), and notes that it replaces 4-6 sequential calls. No contradictions with 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 long but every sentence adds value: trigger phrases, use case, routing details, verdict definitions, and error semantics. It is well-structured, front-loaded with the most actionable trigger phrases, and uses punctuation effectively to pack information. Slightly verbose but justified by the tool's complexity.

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?

There is no output schema, so the description carries full responsibility for explaining return values. It enumerates all verdict types, mentions the actual value and citation, explains the meaning of error variants ('could_not_verify' vs. 'unsupported'), and describes the internal routing. This is exceptionally complete for a tool with two parameters and no output schema.

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 descriptions already cover both parameters (claim and tolerance_pct) with examples and constraints (100% coverage). The description adds no direct parameter-specific information beyond the schema; it mentions percent-delta math but does not clarify tolerance_pct further, so baseline 3 is appropriate.

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 function: natural-language claim verification against authoritative sources, using specific trigger phrases like 'fact check' and 'verify the claim that.' It distinguishes itself from sibling tools by emphasizing the verdict output and its role in replacing multiple sequential calls, making its purpose unambiguous.

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 to use it whenever the agent needs to check whether something a user said is factually correct, providing clear context for usage. It does not name specific alternative tools or provide when-not-to-use exclusions, but the routing explanation (company-financial vs. other claims) gives additional situational 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

Several tools form overlapping families: ask_pipeworx and ask_pipeworx_beta are currently identical, ask_pipeworx_grounded and deep_research cover much of the same router territory, and five Polymarket tools overlap heavily on edge/arbitrage analysis. The descriptions are detailed, but an agent would regularly need to compare multiple near-equivalent candidates before choosing one.

Naming Consistency3/5

Names are consistently snake_case and readable, but the conventions vary widely: verb_noun (search_papers, resolve_entity), noun phrases (entity_profile, polymarket_arbitrage, recent_changes), bare verbs (remember, subscribe, forget), and suffixed variants (ask_pipeworx_beta, ask_pipeworx_grounded). It is not chaotic, but there is no single predictable pattern.

Tool Count2/5

Thirty-five tools is far too many for a server named Paperswithcode, especially since only four tools actually relate to papers while the rest cover data routing, prediction markets, memory, subscriptions, npm auditing, llms.txt generation, and AI visibility. The count feels bloated and the scope unfocused relative to the server's apparent identity.

Completeness3/5

The paper-discovery subdomain is reasonably covered with search, trending, detail, and implementation lookup, and the broader set includes discovery, memory, subscription lifecycle, and feedback tools. However, the overall surface is a patchwork of unrelated domains with no well-defined boundary, and paper datasets/models can only be counted rather than directly listed. Agents can work around most gaps, but the coverage is uneven.

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