Skip to main content
Glama

Kegg

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

A4.7/5.0
Behavior5/5

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

Annotations already mark the tool as readOnly, openWorld, idempotent, non-destructive. The description adds valuable post-verification behavior: the distinction between could_not_verify (check did not happen, must not be used as evidence) and unsupported (no source exists), plus the presence of verification_error with stage/detail. It also discloses the automatic fallback pipeline and citation mechanics, going well 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 every sentence earns its place: triggers, use cases, routing, verdicts, error semantics, and efficiency note. It is front-loaded with examples and ends with a clear 'IMPORTANT' caveat. Structure is logical, though slightly dense for a casual reader.

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?

Given the tool's complexity (routing, verdict types, error states) and the absence of an output schema, the description fully covers return values, evidence format, and failure semantics. It explains the difference between could_not_verify and unsupported, which is critical for agent decision-making. No gaps remain.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Even though the schema covers 100% of parameters with descriptions, the tool description enriches both. For claim, it provides realistic examples. For tolerance_pct, it explains the override semantics and gives a use case (1–2 for hallucination detection) and a default cap of 5%. This adds meaning beyond the bare schema.

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 concrete natural-language triggers ("Is it true that…", "fact check") and specifies the action: natural-language claim verification against authoritative sources. It clearly distinguishes from sibling tools by stating it's for verifying factual correctness and even notes it replaces multiple sequential calls.

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?

Explicitly states when to use: 'Use whenever the agent needs to check whether something a user said is factually correct.' It gives a detailed breakdown of financial vs. other claims and the routing behavior. No explicit exclusions for when not to use, but the context is strong enough to infer boundaries.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Kegg toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.6/5.0
Disambiguation2/5

The three ask_pipeworx variants heavily overlap, with ask_pipeworx_beta currently matching ask_pipeworx exactly, and the six polymarket_* tools have blurry boundaries between scanning, arbitrage, and fill-risk analysis. The many non-KEGG research tools also make it easy to confuse unrelated purposes with the server's nominal bioinformatics focus.

Naming Consistency3/5

All names use snake_case, but the conventions vary widely: single verbs (find, remember, subscribe), verb_noun pairs (get_entry, resolve_entity), noun phrases (entity_profile, bet_research), and vendor prefixes (pipeworx_*, polymarket_*). It is readable but not a consistent, predictable pattern.

Tool Count1/5

34 tools is far beyond the well-scoped range, and the server is named 'Kegg' while only 3 of the 34 tools actually relate to KEGG bioinformatics. The rest belong to an unrelated Pipeworx data-research and prediction-market platform, an extreme mismatch between name, purpose, and tool count.

Completeness2/5

For a KEGG server, the surface is severely incomplete: only find, get_entry, and list_database exist, with no batch retrieval, cross-database queries, or pathway-organism mapping. The broader Pipeworx toolset is rich for data research but entirely disconnected from the server's stated purpose, leaving the actual domain under-covered.

Resources