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Datecalc

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 declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false, but the description adds substantial behavioral context: two distinct processing paths, a detailed verdict enum, the meaning of could_not_verify versus unsupported, and the inclusion of verbatim evidence with citations. It also explains that could_not_verify is not evidence either way, which is crucial to avoid misuse.

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?

Although the description is long, every sentence serves a distinct purpose: usage triggers, routing logic, return payload, error semantics, and value proposition. It opens with concrete example queries to immediately orient the caller, and the information is tightly packed without 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?

Given the tool's complexity, the description is remarkably complete. It covers when to use, how it routes different claim types, the return verdicts, the meaning of each outcome, the citation format, and parameter semantics. No output schema exists, so the description appropriately carries the burden of explaining return values and error handling.

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 schema already provides 100% parameter coverage with clear descriptions for both 'claim' and 'tolerance_pct'. The description adds extra value by clarifying the default tolerance behavior, the override semantics, and the recommended range for hallucination detection. This goes beyond simply restating the schema, though the schema already does much of the work.

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 uses a specific verb (verify/check) and resource (natural-language factual claims against authoritative sources). It clearly distinguishes this tool from siblings like ask_pipeworx_grounded or deep_research by specifying its unique role: taking a claim and returning a verdict, with a structured fast path for company-financial claims. Replacing 4–6 sequential calls further clarifies its scope.

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 provides explicit context for when to use the tool: whenever the agent needs to check whether something a user said is factually correct. It also details the routing behavior for different claim types. However, it doesn't explicitly name alternatives or state when not to use this tool, so it stops short of a full 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

Many tools overlap in purpose, such as the multiple Polymarket tools (bet_research, polymarket_arbitrage, polymarket_edges, etc.) and the several query tools (ask_pipeworx, ask_pipeworx_grounded, deep_research). Memory tools (remember, recall, forget) also add to the confusion.

Naming Consistency3/5

Tool names are mostly snake_case but vary in pattern: some are verb_noun (add_duration, compare_entities), others are noun_noun (entity_profile, date_diff) or single verbs (forget). This mix reduces predictability but is not chaotic.

Tool Count2/5

The server name 'Datecalc' implies a narrow focus, yet 33 tools exist covering far more than date calculations. The count is too high for the implied purpose, and many tools are unrelated to the server's apparent domain.

Completeness2/5

For the implied date calculation domain, only three tools exist (add_duration, date_diff, date_info), leaving obvious gaps. For the actual broad data access domain, coverage is better but still lacks a cohesive structure.

Resources