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Opencellid

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?

The description adds critical behavioral context beyond the annotations: it explains the distinction between could_not_verify (the check did not happen, not evidence either way) and unsupported (no coverage), and discloses that returns include a verdict, the actual value with citation, and reasoning. It also notes the tolerance_pct override for hallucination detection. These details meaningfully supplement the readOnly/openWorld/idempotent hints.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately sized for the tool's complexity. It opens with concrete user query examples, then defines the action, explains the two routing paths, summarizes the output, highlights the crucial could_not_verify caveat, and closes with the efficiency benefit. Every sentence adds information; there is no fluff or 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?

For a tool with two pipeline paths and nuanced verdict meanings, the description covers all essential details: the claim types (financial vs. other), the return structure (verdict, value, citation, reasoning), the special handling of could_not_verify and unsupported, and the tolerance override. It is complete enough for an agent to invoke correctly without an output schema, and it explains the optional parameter's impact.

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?

Schema coverage is 100% (both parameters described), so baseline is 3. The description elevates this by explaining tolerance_pct's purpose beyond the schema: it 'overrides the tolerance implied by the claim wording' and recommends setting 1–2% for hallucination detection. The claim parameter already has illustrative examples in the schema, so the description's additional examples are marginal but the tolerance_pct enrichment is valuable.

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 defines the tool as 'natural-language claim verification against authoritative sources' and lists recognizable query patterns like 'fact check' and 'is it true that'. It distinguishes itself by describing two routing paths (SEC EDGAR for company-financial claims vs. grounded pipeline for all others), making it clear this is a specialized claim-verification tool, not a general Q&A tool.

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 explicitly states 'Use whenever the agent needs to check whether something a user said is factually correct,' which gives clear guidance. It also explains the routing logic and notes that the tool 'replaces 4–6 sequential calls,' implying it should be preferred over composing other tools. However, it does not name specific sibling tools to exclude, so it lacks an explicit 'when-not-to-use' statement.

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

Several tools are near-indistinguishable by role: ask_pipeworx and ask_pipeworx_beta are documented as currently identical, and ask_pipeworx, ask_pipeworx_grounded, and deep_research overlap as lookup/research entry points. The six-tool Polymarket cluster and the suggest_questions/discover_tools pair add further boundary confusion despite very long descriptions.

Naming Consistency3/5

Snake_case is used consistently, and clusters like ask_pipeworx_* and polymarket_* have internal consistency. However, the global convention is mixed: verb_object names (get_cell, resolve_entity, unsubscribe) sit beside noun phrases (entity_profile, recent_changes, cells_in_area) and product-prefixed nouns (pipeworx_feedback, polymarket_edges), so tool names are not predictable from function.

Tool Count2/5

33 tools is too many for a server named Opencellid, especially since only get_cell and cells_in_area actually belong to the cell-tower domain. Even as a broader Pipeworx/data bundle, the set is heavy and includes unrelated one-offs like generate_llms_txt and scan_dependency.

Completeness2/5

For an OpenCellID server, the surface is just two lookups, missing coverage stats, operator-based search, and other natural cell-tower operations. If judged instead as a Pipeworx data-research suite, coverage is broad, but the lack of a coherent stated domain makes obvious gaps and dead ends harder to identify.

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