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Flickr Public

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?

Beyond the annotations (readOnly, openWorld, idempotent), the description details behavior: company-financial claims go through SEC EDGAR/XBRL with exact math; other claims fall through to a grounded pipeline. It defines the meaning of could_not_verify (not evidence) and unsupported (no source found), which is crucial context. No contradiction with 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?

While longer than typical tool descriptions, every sentence adds value: trigger phrases, routing, verdict definitions, and error semantics. It is front-loaded with purpose and structured logically, with no redundant filler.

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?

The description covers return values (verdict types, citation, reasoning), error behavior (could_not_verify vs unsupported), and the two execution paths. Given there is no output schema, this level of detail fully prepares the agent for invocation and interpretation.

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 baseline is 3. The description mentions tolerance_pct's role in grading and hallucination detection, but this is already captured in the parameter descriptions. No additional semantic meaning beyond the schema.

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 trigger phrases and clearly states 'natural-language claim verification against authoritative sources,' specifying a concrete action (verify) and resource (claims). It distinguishes itself from siblings by emphasizing verdict output and the two-path routing (SEC EDGAR vs grounded), making the tool's purpose unambiguous.

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 says 'Use whenever the agent needs to check whether something a user said is factually correct,' providing clear applicability. It also notes the tool replaces 4–6 sequential calls, implying it should be chosen over chaining other tools, though it does not name specific alternative tools or list exclusions.

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

B3.1/5.0
Disambiguation1/5

The tool set is extremely confusing because the server name 'Flickr Public' suggests a photo-sharing focus, but the vast majority of tools are for financial data, prediction markets, and other unrelated domains. Only 3 out of 33 tools are about Flickr, making it nearly impossible for an agent to understand the server's purpose or select appropriate tools.

Naming Consistency2/5

Tool names are all in snake_case, which is consistent, but there is no semantic pattern across the set. Verbs vary widely (ask, bet, by, compare, deep, discover, etc.) and many tools have descriptive but overly long names, mixing different styles. The lack of a common naming framework adds to the confusion.

Tool Count1/5

With 33 tools, the count is far too high for a server that should be about Flickr. The scope is massively overextended, covering multiple unrelated domains like SEC filings, Polymarket betting, and drug data. This mismatch makes the tool count feel bloated and inappropriate for the server's stated purpose.

Completeness1/5

For a Flickr server, the tool surface is severely incomplete. It only offers basic read operations (by_group, by_user, recent) and lacks core Flickr functionality such as uploading, editing, searching photos, or managing albums. The addition of hundreds of unrelated tools does not compensate for this gap, leaving the server's primary domain largely unaddressed.

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