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Chembl

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 declare readOnly, idempotent, and openWorld, but the description adds crucial behavioral semantics: the distinction between could_not_verify (check did not happen) and unsupported (no source), the requirement not to treat could_not_verify as evidence, and the two routing paths (SEC EDGAR structured vs grounded). No contradiction 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 carries operational value: trigger phrases, routing details, return format, error semantics, and replacement of multi-call workflows. It is well-structured, front-loading with examples and ending with practical notes, though slightly dense.

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?

Despite having no output schema, the description fully specifies the verdict values, evidence format (grounded value + citation), reasoning output, and error handling. It also covers the two workflow paths and edge-case meanings, making the tool self-contained for an agent.

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?

Input schema covers both parameters with detailed descriptions (100% coverage), including tolerance_pct defaults and override behavior. The description reinforces the tolerance meaning but does not add substantially new parameter-level semantics beyond what the schema already provides.

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 opens with explicit trigger phrases ('fact check', 'verify the claim that…') and clearly states the tool's function: 'natural-language claim verification against authoritative sources.' It distinguishes itself from siblings like search and deep_research by scoping to factual claim checking rather than open-ended exploration.

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 '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 and notes the tool replaces 4–6 sequential calls. However, it does not explicitly name alternatives or state when NOT to use it (e.g., for broad research), 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

B3.4/5.0
Disambiguation2/5

The tool list mixes a small ChEMBL dataset with a large Pipeworx toolkit, and several Pipeworx tools are near-duplicates (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded; polymarket_arbitrage, polymarket_edges, polymarket_fill_risk). An agent would struggle to pick the right tool among overlapping prediction-market and research tools, and the ChEMBL tools are buried under irrelevant functionality.

Naming Consistency2/5

Naming conventions are mixed: ChEMBL tools use bare nouns (molecule, target, activities) while Pipeworx tools use inconsistent verb_noun phrases (ask_pipeworx, validate_claim) and noun phrases (entity_profile, recent_changes). There is no predictable pattern across the set.

Tool Count2/5

37 tools is far too many for a server named 'Chembl', especially since the majority are unrelated Pipeworx features. The count is justified neither by the apparent ChEMBL scope nor by a coherent overall purpose, making the server feel bloated and unfocused.

Completeness3/5

The ChEMBL subset is reasonably complete (search, molecule, target, activities, mechanism, drug_indications), and the Pipeworx side includes broad research/data tools, but the set lacks a unified purpose. Gaps include no direct assay/detail retrieval and no coherent lifecycle across the mixed domains.

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