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Packagephobia

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

A4.8/5.0
Behavior5/5

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

Annotations already cover readOnly/openWorld/idempotent/non-destructive, so the description adds the crucial behavioral nuance of verdict semantics: 'could_not_verify means the check did not happen... it is NOT evidence for or against the claim.' It also discloses error handling via verification_error and the fallback from EDGAR to the grounded pipeline, going well beyond annotation-provided safety signals.

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 fairly long but each sentence adds distinct value: trigger phrases, usage rule, routing logic, verdict semantics, error differentiation, and a replacement note. It is front-loaded and structured so the agent quickly understands when to invoke it, and the extra length is justified by the tool's complexity.

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 must explain return values; it does, enumerating the verdict types, the actual value with citation, and reasoning. It also covers the two pipeline paths and the error case, making it sufficiently complete for reliable tool selection and invocation.

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?

Schema coverage is 100%, but the description adds behavior beyond the schema: it explains that tolerance_pct overrides wording-implied tolerance, recommends 1–2% for hallucination detection, and states a default cap of 5%. This enriches the bare schema definition with operational guidance.

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 trigger phrases and states the tool performs claim verification against authoritative sources. It explicitly distinguishes company-financial claims via SEC EDGAR/XBRL from other facts, and names the verdict types, making the tool's function unmistakable and distinct from sibling tools like deep_research or ask_pipeworx.

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 guidance: 'Use whenever the agent needs to check whether something a user said is factually correct.' It also explains the routing logic and notes it replaces multiple sequential calls, but it does not name specific alternative tools or state when not to use it, 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

A3.8/5.0
Disambiguation2/5

Several near-overlapping query tools exist: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route to the same underlying catalog, and ask_pipeworx_beta is currently described as identical to ask_pipeworx. The Polymarket tools (bet_research, polymarket_edges, polymarket_arbitrage, polymarket_fill_risk, polymarket_edge_tracker) also heavily overlap and rely on lengthy descriptions to keep them apart.

Naming Consistency3/5

The set is uniformly snake_case and generally readable, but conventions are mixed: verb_noun (resolve_entity, suggest_questions), noun_verb (bet_research, ai_visibility_check), noun_adj (pipeworx_trending, recent_changes), and bare verbs (size, forget, recall) all appear. Variant suffixes like ask_pipeworx_beta and ask_pipeworx_grounded add further unpredictability.

Tool Count2/5

32 tools exceeds the 'too many' threshold and spans unrelated domains: package size, general data research, prediction markets, memory, and subscription management. The set would feel more coherent at roughly half the count, with several query and Polymarket tools consolidated.

Completeness3/5

For the dominant data-research purpose, the surface is reasonably complete: querying, grounded verification, deep research, entity resolution, entity profiles, comparisons, claim validation, and discovery are all covered. However, the server's stated identity ('Packagephobia') is nearly absent—only `size` and `scan_dependency` address package sizing—so the namesake domain is thin while unrelated domains are overbuilt.

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