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Glama

Semver

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 signal read-only, open-world, idempotent, non-destructive, but the description adds crucial behavioral nuance: could_not_verify means the check did not happen and must not be shown as evidence for/against, and unsupported means no source was found. It also discloses the two distinct processing paths (SEC EDGAR + XBRL fast path vs. grounded pipeline) and the return type with citation, which goes 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.

Conciseness5/5

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

The description is well-structured: opens with natural-language examples, states purpose, explains dual paths, details return types, and flags an important caller caution. Every sentence carries weight, and the section on could_not_verify is essential. It is neither bloated nor under-specified.

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?

Despite having no output schema, the description fully explains return values (five verdict types plus actual value and citation), the meaning of error states, the routing logic, and the efficiency advantage. It gives an agent enough context to invoke the tool correctly and interpret results, which is complete for a 2-parameter read-only tool.

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%, so baseline 3 would apply, but the description adds significant parameter guidance: tolerance_pct's semantics are expanded (overrides implied tolerance, set 1–2 for hallucination detection, default capped at 5), and the claim parameter is given concrete examples. This is valuable usage-level detail not present in the schema alone.

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's purpose: "natural-language claim verification against authoritative sources" with a specific verb (validate) and resource (claim). It distinguishes itself from siblings by focusing on fact-checking with verdicts and explicitly noting it replaces 4–6 sequential NL parsing→entity resolution→data lookup→comparison calls, which no sibling tool describes.

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 gives explicit when-to-use context: "Use whenever the agent needs to check whether something a user said is factually correct." It further clarifies two routing paths (company-financial vs. any other factual claim). It doesn't explicitly name alternative tools for non-fact-checking tasks, but the strong directive plus the efficiency note provides clear guidance.

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

A3.7/5.0
Disambiguation2/5

Several clusters overlap significantly: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all serve general data-query purposes, and entity_profile, compare_entities, recent_changes, and validate_claim pull from the same SEC/news/data sources in similar ways. The semver utilities are distinct, but they sit alongside unrelated prediction-market, memory, subscription, and AI-visibility tools that make the overall boundary of each tool much fuzzier.

Naming Consistency3/5

Some tools follow a clean verb_noun pattern (parse_semver, compare_semver, compare_entities, resolve_entity), but others are noun phrases (entity_profile, polymarket_edges, recent_changes) or branded/verb-first names (ask_pipeworx, deep_research, bet_research, pipeworx_trending). The mix is readable but inconsistent, with no unifying convention across the 34 tools.

Tool Count2/5

34 tools is too many for a server named Semver, whose actual semver-related surface is only a few utilities. Even viewed as a broad data platform, the count is heavy and padded with unrelated capabilities like prediction-market arbitrage, memory storage, subscriptions, and AI-visibility checks that do not belong together in one server.

Completeness2/5

As a Semver server it covers parse, compare, and range satisfaction but lacks obvious operations like version bumping/incrementing or validating a version list, making the core surface incomplete. As a general data platform the domain is unclear and the unusual mix of semver, market, memory, and marketing tools prevents any coherent completeness assessment.

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