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Glama

Ebird

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

A4.5/5.0
Behavior5/5

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

The annotations already cover read-only, idempotent, and non-destructive traits, and the description adds substantial behavioral context well beyond that: the meaning of 'could_not_verify' (check did not happen, not evidence), the distinction from 'unsupported', the presence of verification_error{stage,detail}, and the exact routing logic. This is rich, non-redundant disclosure that aids correct caller interpretation.

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 long but every sentence earns its place: trigger phrases, usage context, two-path routing, return values, and a critical caller caveat are all packed efficiently. It begins with the most recognizable trigger phrases and ends with a practical note about replacing sequential calls, maintaining a clear logical flow.

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 lacking an output schema, the description fully enumerates the possible verdicts and explains the two ambiguous ones in detail. It covers the two distinct processing paths, the tolerance mechanism, and error semantics. For a tool with this complexity, the description is remarkably complete and leaves no major gaps for the caller.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the schema already documents both parameters thoroughly. The description adds a couple of concrete claim examples and clarifies that tolerance_pct can be used for hallucination detection, but these are minor additions. Baseline 3 is appropriate since the description does not need to compensate for missing schema info.

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: verifying natural-language factual claims against authoritative sources, with specific verbs like 'fact check' and 'verify the claim that…'. It distinguishes itself from sibling tools by focusing on claim verification rather than general Q&A, and even differentiates two processing paths for company-financial versus other claims.

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?

Provides explicit guidance on when to use: 'Use whenever the agent needs to check whether something a user said is factually correct.' It also explains the routing logic for different claim types (SEC EDGAR vs. grounded pipeline) and notes it replaces 4–6 sequential calls. However, it does not explicitly mention sibling alternatives or state when not to use it, so a small gap remains.

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
Disambiguation3/5

Several tools are close cousins: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route to the same underlying data catalog, and bet_research/polymarket_edges/polymarket_arbitrage share a betting-research niche. The eBird tools are clearly a different cluster, but the server carries so many unrelated domains that an agent may struggle to pick the right category member (e.g., stable router vs beta router vs grounded router).

Naming Consistency3/5

Almost everything is snake_case, but the pattern is not uniform: there are plenty of verb_noun names (find_species, list_subregions, scan_competitor_ai_presence) mixed with bare consumer-style names (ask_pipeworx, bet_research, entity_profile, deep_research) and short helpers (recall, forget, remember). No mixed scripting-case chaos, but no consistent verb_noun or noun_verb system either.

Tool Count2/5

36 tools is heavy for a server that calls itself Ebird: only ~5 tools actually relate to bird observation, while the rest span Pipeworx data lookups, Polymarket betting, legal/regulatory analyzers, memory, subscriptions, competency scanning, npm package checking, and llms.txt generation. The count would be reasonable for a broad data platform, but the server's stated identity and the bundled tool set do not match, making the scope feel bloated and incoherent.

Completeness2/5

For a bird-centric server, the surface is thin: you can find a species, list subregions, and pull recent/notable observations, but you cannot get coordinated eBird atlases, hotspot details, species life-history stats, or region-based species lists. For the larger set of unrelated tools, completeness is impossible to gauge about a missing domain; the eBird purpose feels unfinished even though the miscellaneous tools are overloaded.

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