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Glama

Conceptnet

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

A4.6/5.0
Behavior5/5

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

Beyond the readOnly/openWorld/idempotent annotations, the description discloses critical non-obvious behavior: 'could_not_verify' means the check did not happen and includes a verification_error object, while 'unsupported' means no source covers the claim. This prevents misinterpretation and adds real value beyond 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 ideal but every sentence contributes distinct information: example intents, usage scope, routing logic, return payload, error semantics, and performance benefit. It is front-loaded with the query patterns and logically organized, though a tighter edit could remove minor redundancy.

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?

There is no output schema, so the description compensates well by specifying the verdict set, the grounded/structured value with pipeworx:// citation, and reasoning. It also explains the two failure-like verdicts and the tolerance behavior. This is comprehensive for a complex tool.

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?

The input schema already fully documents both parameters (claim and tolerance_pct), so the baseline is 3. The description adds practical nuance—tolerance_pct overrides the wording-implied tolerance and recommends setting 1–2 for hallucination detection—offering guidance beyond the 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 clearly states the tool performs natural-language claim verification against authoritative sources, provides example phrasings, and distinguishes itself from siblings by detailing the two processing paths (SEC EDGAR/XBRL for financial claims, grounded pipeline for others) and noting it replaces 4–6 sequential calls. This is a specific verb+resource with clear differentiation.

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 says to use whenever the agent needs to check whether something a user said is factually correct, and divides claims into company-financial vs any other, giving clear scope. It does not explicitly name sibling tools as alternatives or state when-not-to-use, so it stops 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

A3.5/5.0
Disambiguation2/5

Several tools have heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-identical, and the five polymarket_* tools (arbitrage, edges, edge_tracker, fill_risk, kalshi_spread) all target prediction-market opportunities. The memory tools (remember/recall/forget) are distinct, as are subscriptions, but the routing and Polymarket clusters create real misselection risk.

Naming Consistency3/5

All names are snake_case, but the pattern is mixed: some are verb_noun (ask_pipeworx, compare_entities, discover_tools, resolve_entity), some are noun_verb or adjective_noun (bet_research, entity_profile, pipeworx_trending, recent_changes), and some are bare nouns/verbs (lookup, query, recall, relatedness). The 'polymarket_*' and 'ask_pipeworx*' families are consistent internally, but the overall set lacks a uniform verb-first convention.

Tool Count2/5

With 34 tools, the server is heavily over-scoped for a server named 'Conceptnet' — the vast majority of tools belong to a separate Pipeworx data-access product, not a semantic-network API. While each tool has a use, the count feels bloated and the server name misrepresents the actual surface.

Completeness4/5

The ConceptNet portion is complete (lookup, query, relatedness cover graph traversal). The Pipeworx portion is also fairly complete: universal router, grounded mode, deep research, entity profiles, comparisons, claim validation, subscription lifecycle, memory, and feedback. Minor gaps exist (e.g., no direct SEC filing fetch without going through ask_pipeworx), but the meta-tools mostly fill them.

Resources