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Glama

Dblp

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

A4.9/5.0
Behavior5/5

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

Annotations indicate read-only and idempotent, but the description adds substantial behavioral nuance: could_not_verify means the check did not happen (with verification_error), unsupported means no source was found, and the verdict set. It clarifies important caller responsibilities and does not contradict annotations.

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 and front-loaded with trigger phrases in the first sentence, then logically organized into purpose, usage, returns, and important caveats. Every sentence provides necessary operational detail without redundancy, making the length appropriate for 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?

No output schema exists, but the description covers the return values (verdict types, actual value, citation, reasoning) and explains error semantics (could_not_verify vs unsupported). It also describes both the structured financial path and the grounded fallback, making the tool fully understandable for an agent.

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?

Schema already covers both params at 100%, so the baseline is 3. The description adds value by explaining tolerance_pct overrides the claim-wording default, suggesting 1-2 for hallucination detection, and noting the default cap of 5. This goes beyond the schema's literal parameter definitions.

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 performs natural-language claim verification against authoritative sources, with specific trigger phrases like 'fact check' and 'verify the claim that...'. It distinguishes itself by focusing on verifying user statements and mentioning it replaces a multi-step lookup pipeline, setting it apart from siblings like deep_research or ask_pipeworx.

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 instructs to use 'whenever the agent needs to check whether something a user said is factually correct.' It details two paths (company-financial vs all other claims) and explains fallback behavior, giving clear context for when to choose this tool over alternatives.

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
Disambiguation3/5

Many tools overlap in general purpose—ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and validate_claim all retrieve factual data—though their descriptions draw clear mode distinctions. The Polymarket family is similarly dense but each member has a distinct role. An agent must read carefully to pick the right one, but the boundaries are mostly decipherable.

Naming Consistency4/5

Tool names overwhelmingly follow snake_case verb_noun or domain_noun patterns (search_publications, resolve_entity, polymarket_edges, ask_pipeworx). Minor exceptions like the bare verbs recall and forget break the pattern slightly, and mixed prefixes (pipeworx_, ask_, search_) are still predictable.

Tool Count2/5

At 34 tools, the set is heavy, but the deeper problem is scope mismatch: the server is named Dblp yet only 3 of 34 tools (search_authors, search_publications, search_venues) relate to DBLP. The remaining 31 tools form a broad general-purpose data platform that dwarfs and obscures the apparent purpose.

Completeness2/5

For a DBLP server, the surface is minimal: only search operations exist, with no record fetch-by-id, citation metrics, or author profile detail beyond what search returns. The bulk of the toolset addresses unrelated domains, leaving the actual DBLP workflow thin and incomplete.

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