Skip to main content
Glama

Linkedin Humblebrag

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

A4.8/5.0
Behavior5/5

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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive. The description adds critical behavioral context: the dual-path routing, the verdict categories, the requirement to cite evidence with pipeworx://, and especially the distinction between could_not_verify (check did not happen, with verification_error) and unsupported (no source covers it). It also warns callers against misinterpreting could_not_verify as evidence, which is crucial. This goes well beyond the structured metadata.

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?

Though longer than average, every sentence earns its place. It opens with highly recognizable trigger phrases, quickly moves to the routing logic, then enumerates return values and error semantics, and closes with the efficiency benefit (replaces 4–6 sequential calls). There is no filler; the structure is logical and front-loaded with the most important information.

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 carries the full burden of explaining return values. It provides the complete list of verdicts, the actual value with citation, reasoning, and the critical error handling distinction. Given the tool's complexity (two processing paths), the description is remarkably complete: an agent knows exactly what to expect and how to interpret results. It also explains the trigger conditions and parameter overrides, making it self-sufficient.

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% for both parameters, but the description adds substantial semantic value beyond the schema. For tolerance_pct, it explains that it overrides the tolerance implied by the claim wording, provides a concrete use case (set 1–2 for hallucination detection), and states the default cap at 5. The claim parameter is illustrated with two realistic examples, clarifying expected input format.

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 uses a specific verb 'validate' with a clear resource: 'natural-language claim verification against authoritative sources.' It lists trigger phrases ('fact check', 'verify the claim that…'), making it immediately identifiable. It differentiates from sibling tools by specifying the structured SEC EDGAR/XBRL path for company-financial claims and a grounded pipeline for other claims, which is distinct from general-purpose ask_pipeworx or deep_research tools.

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 states 'Use whenever the agent needs to check whether something a user said is factually correct' and provides routing guidance for company-financial vs. other claims. However, it does not explicitly name when NOT to use this tool or mention alternative tools (e.g., ask_pipeworx_grounded), so it falls short of the 5 criterion for explicit exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Linkedin Humblebrag toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.7/5.0
Disambiguation3/5

Most tools are distinctly named and the descriptions are unusually specific, but the set contains overlapping families: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route to the same underlying data, and the beta variant is currently an exact duplicate. The entity/company lookup, AI-visibility, and Polymarket clusters also require reading the long descriptions to choose correctly.

Naming Consistency3/5

All names are lowercase snake_case and readable, with useful prefixes like ask_pipeworx, polymarket_, and pipeworx_. However the macro pattern is mixed: many are verb-first (compare_entities, resolve_entity), many are noun phrases (entity_profile, polymarket_edge_tracker, recent_alerts), and one puts the verb last (linkedin_humblebrag_generate).

Tool Count2/5

32 tools is far too many for a server whose apparent name and stated LinkedIn-humblebrag purpose are served by exactly one tool. Even viewed as a general Pipeworx/research utility, the surface is bloated: duplicate ask variants, multiple meta-tools, and a sprawling prediction-market family push the count well past the 25-tool threshold.

Completeness2/5

For the domain implied by the server name, the surface is severely incomplete: only generation exists, with no way to list, edit, delete, publish, or manage LinkedIn-humblebrag posts. The de facto Pipeworx research domain is much better covered, but the overall set has serious dead ends because the one LinkedIn tool is isolated and the core tools are oriented toward a different, unrelated workflow.

Resources