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Ensembl

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?

Annotations already mark it readOnly and non-destructive, but the description adds critical context: the meaning of 'could_not_verify' (check did not happen, not evidence) and 'unsupported' (no source found). It also discloses the routing behavior and the return of verdict, evidence, and reasoning, which goes beyond the 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 longer than average but well-structured: front-loaded with trigger phrases, then purpose, routing, outcome meanings, and efficiency gains. Each sentence adds necessary information for a complex tool, though some redundancy exists (e.g., 'grounded pipeline' explanation).

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 fully covers the return values: verdict enum, actual value with citation, reasoning, and special handling for 'could_not_verify' and 'unsupported'. It also explains the two routing paths and the efficiency benefit, making it complete for an AI agent to select and invoke correctly.

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?

Both parameters are fully described in the input schema (100% coverage), so the baseline is 3. The description's example for 'claim' adds a concrete illustration, and 'tolerance_pct' is already well-defined in the schema. No additional parameter-level semantics are provided in the description.

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 explicitly states the tool performs 'natural-language claim verification against authoritative sources' with clear trigger phrases ('is it true that...', 'fact check'). It distinguishes two execution paths (SEC EDGAR fast path vs grounded pipeline) and differentiates from sibling tools by focusing on verifying factual claims.

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 guidance to 'use whenever the agent needs to check whether something a user said is factually correct.' It also explains the internal routing for company-financial vs other claims. However, it does not explicitly name alternative tools or state when not to use it, so it falls short of a 5.

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
Disambiguation2/5

Multiple tool families heavily overlap: ask_pipeworx, ask_pipeworx_beta (explicitly identical), ask_pipeworx_grounded, deep_research, and validate_claim all answer natural-language questions, while polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread, and bet_research all analyze prediction markets. An agent would struggle to pick the correct tool without reading very long descriptions.

Naming Consistency3/5

All names are snake_case and readable, but the style is inconsistent: some are bare nouns (sequence, variation, homology), some are single verbs (lookup, recall, forget), and others are long descriptive phrases (scan_competitor_ai_presence, polymarket_kalshi_spread). There is no consistent verb_noun or resource_noun pattern across the set.

Tool Count2/5

38 tools is excessive for a coherent server, and nearly all of them are unrelated to the server's stated name ('Ensembl') — only about 7 tools (lookup, lookup_symbol, sequence, variation, vep, xrefs, homology) actually belong to the Ensembl domain. The rest form several unrelated clusters (Pipeworx data queries, prediction markets, memory, subscriptions), making the tool count feel bloated and unfocused.

Completeness2/5

For an Ensembl server, the surface is thin: it covers ID lookup, sequence retrieval, variants, VEP, xrefs, and homology, but omits other core Ensembl functionality such as gene trees, alignments, regulation, expression, and assembly data. Meanwhile the many non-Ensembl tools don't form a complete domain of their own — they are a grab bag of unrelated utilities.

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