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Cbioportal

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.

TDQS

A4.4/5.0
Behavior5/5

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

The description discloses the dual-pipeline routing, verdict semantics, the crucial distinction between 'could_not_verify' and 'unsupported', and citation behavior. This is significant context beyond the annotations' read-only/idempotent/open-world flags.

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 well-structured and front-loaded with trigger phrases, but at ~300 words it is relatively long. Redundant phrases ('natural-language claim verification' vs 'check whether something a user said is factually correct') prevent a 5, though every functional section is useful.

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 thoroughly enumerates the return values (verdict, value, citation, reasoning) and error handling (could_not_verify vs unsupported). It fully compensates for the absence of an output schema and covers the tool's complexity.

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?

The schema already provides comprehensive descriptions for both parameters, including examples and tolerance range/overrides. The description adds minimal extra meaning (e.g., 'exact percent-delta math') and mostly restates schema content, so a 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 precisely defines the tool as a natural-language claim verifier with specific trigger phrases and a clear outcome (verdict). It distinguishes itself from sibling Q&A tools by focusing on fact-checking and returning structured verdicts.

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?

It gives explicit instruction to use whenever the agent needs to check factual correctness, plus detailed routing logic. However, it does not name alternative tools or explicitly state when NOT to use it, so it lacks the full 'when-not/alternatives' 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

A3.6/5.0
Disambiguation3/5

Most tools have clearly distinct purposes, but ask_pipeworx_beta is an intentional near-duplicate of ask_pipeworx, and several polymarket/entity tools overlap in scope. The descriptions do enough to disambiguate most pairs, but the duplicate beta routing tool introduces real ambiguity.

Naming Consistency3/5

All names use snake_case, but the pattern varies: verb_noun (get_gene, search_studies), noun_noun (polymarket_edges, pipeworx_trending), and product-prefixed verbs (ask_pipeworx, bet_research). There is no single consistent convention, though the names remain readable.

Tool Count2/5

35 tools is a heavy surface, and the vast majority (31) are unrelated to cBioPortal; only four tools actually belong to the named domain. This makes the count inappropriate for a cancer-genomics MCP server, as the set is bloated with out-of-scope utilities.

Completeness1/5

For a cBioPortal server, only metadata-level tools exist (gene lookup, study details, cancer types, study search); core cBioPortal data access — mutations, copy-number alterations, clinical data, molecular profiles, sample-level queries — is entirely missing. The tool surface severely under-covers the named domain.

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