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Linkedin Humblebrag

by io.github.pipeworx-io

Recent Alerts

recent_alerts
Read-onlyIdempotent

Pull fired events from your subscription feed. Returns the most recent alerts the evaluator has written to your persisted feed — each carries source, citation_uri (pipeworx:// when available), and the raw event payload. Filter by type (e.g. "sec_8k") and/or since (ISO timestamp). Set mark_read:true to flag returned events read so the next call only shows newer ones. Polls work fine; the same feed is also at GET registry.pipeworx.io/alerts.json for scripts and dashboards.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeNoOptional — filter to one subscription type.
limitNoMax events to return (1-200, default 50).
sinceNoOptional ISO timestamp — return events fired_at >= this time.
mark_readNoFlag the returned events read in the same call (default false).
unread_onlyNoReturn only events where read_at is null (default false).

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Added

TDQS

A3.8/5.0
Behavior1/5

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

Annotations state readOnlyHint:true, but the description says 'Set mark_read:true to flag returned events read so the next call only shows newer ones.' This is a state-changing operation that directly contradicts the read-only annotation. The description also lacks disclosure of other behavioral traits like pagination or error semantics, but the contradiction alone forces a score of 1.

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?

Three sentences, each earning its place: the first states purpose and return content, the second explains filtering and mark_read behavior, and the third gives polling guidance and an alternative endpoint. No filler or repetition.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers the main usage scenarios, the shape of the returned payload, persistence, and the alternative feed URL. It does not mention limit defaults or unread_only, but these are already in the schema. Given the tool's moderate complexity and strong schema, the description is nearly complete, though the mark_read contradiction creates a gap in accurate guidance.

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

Parameters4/5

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

Schema already covers all parameters (100% coverage), so baseline is 3. The description adds value by giving examples for type ('sec_8k'), clarifying the ISO timestamp format for since, and explaining the side effect of mark_read on future calls. This goes beyond the schema descriptions.

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 the specific verb 'Pull' and the resource 'fired events from your subscription feed', clearly distinguishing this from sibling tools like list_subscriptions. It also details what the returned events contain (source, citation_uri, raw payload), making the tool's purpose concrete.

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 provides clear context on when to use the tool: for polling recent alerts, and explicitly mentions an alternative HTTP endpoint for scripts/dashboards. However, it does not name specific sibling tools to prefer over this one, so it stops short of full exclusion 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
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.

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