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Linkedin_ads

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. Added

TDQS

A4.5/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds substantial behavioral context beyond that: it enumerates all possible verdicts, explains that could_not_verify means the check did not happen and carries verification_error, and clarifies that unsupported means no source exists. It also discloses the fast-path vs grounded pipeline behavior. 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?

The description is dense but every sentence earns its place: examples, when-to-use, internal routing, return types, and a caller-critical warning about could_not_verify. It is appropriately sized for a fact-checking tool with multiple paths, and information is front-loaded with natural-language triggers.

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?

With no output schema, the description fully explains return semantics: verdict values, actual value with pipeworx:// citation, reasoning, and the meaning of error/inconclusive states. It also covers the two distinct claim categories and what happens for unsupported claims. This is complete enough for an agent to select and invoke correctly.

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 description coverage is 100%, so the schema already fully documents both claim and tolerance_pct with examples and default behavior. The tool description adds little parameter-specific meaning beyond reinforcing that claim is natural-language and mentions the tolerance override indirectly via verdict grading. Baseline 3 is appropriate because the schema carries the load.

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 identifies a specific verb+resource: natural-language claim verification against authoritative sources. It distinguishes itself from siblings by explicitly covering both structured SEC/XBRL fast-path claims and a grounded fallback pipeline, and notes it replaces 4–6 sequential calls. This is unique among the sibling tools listed.

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 an explicit when-to-use instruction: 'Use whenever the agent needs to check whether something a user said is factually correct.' It also explains internal routing for company-financial vs any other claim. It does not explicitly name alternative tools to avoid or state when not to use this tool, but the positive guidance is clear and specific.

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 have overlapping purposes (multiple ask_pipeworx variants, several polymarket tools, and similar research functions). The agent would struggle to pick the right tool among 36, especially with unclear boundaries between some tools.

Naming Consistency2/5

Tool names lack a consistent pattern. The li_ prefix for LinkedIn tools is isolated; other tools use random prefixes (pipeworx_, polymarket_) or no prefix at all (ask_pipeworx, bet_research). Mix of verb_noun and noun structures.

Tool Count2/5

36 tools for a server named 'Linkedin_ads' is excessive. Only 5 tools are actually LinkedIn ads-related; the rest are unrelated, making the server feel bloated and unfocused. A focused server would have 5-10 tools.

Completeness2/5

For the stated LinkedIn ads domain, the tool set is incomplete: missing create/update/delete campaigns, budget management, and targeting. While the server includes many other data tools, its advertised purpose is poorly served.

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