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Glama

colorapi

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

A3.9/5.0
Behavior1/5

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

The description discloses a state-changing operation: 'Set mark_read:true to flag returned events read so the next call only shows newer ones.' This directly contradicts the readOnlyHint=true annotation, which implies no side effects. This is an annotation contradiction.

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 compact and front-loaded, with four sentences each serving a purpose: explaining the core action, listing returned fields, describing filtering options, and covering polling behavior and an alternative endpoint. No unnecessary words.

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 explicitly mentions the return fields (source, citation_uri, raw payload), covers polling suitability, and cross-references an external endpoint. Combined with the fully described schema parameters, an agent has all necessary information for correct invocation.

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 description coverage is 100%, providing a baseline of 3. The description adds value beyond the schema by giving a concrete type example ('sec_8k'), specifying the since format as ISO timestamp, and explaining mark_read's effect on subsequent calls.

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 'Pull fired events from your subscription feed,' giving a specific verb and resource. It clearly distinguishes the tool from siblings like list_subscriptions and recent_changes by focusing on alert events.

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 explains when to use the tool (for polling alerts) and mentions filters, mark_read behavior, and an alternative REST endpoint. However, it does not explicitly name alternative tools or provide exclusionary 'when not to use' 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

A4/5.0
Disambiguation3/5

Some tools have distinct purposes (e.g., color tools, memory tools), but many data query tools (ask_pipeworx, ask_pipeworx_grounded, deep_research, entity_profile) overlap significantly, making it hard for an agent to choose the right one without deep knowledge of their nuances.

Naming Consistency4/5

Most tool names use snake_case with a verb_noun pattern (e.g., convert_color, identify_color, resolve_entity), but there is inconsistency in prefixes: some use 'ask_pipeworx', others 'pipeworx_', 'polymarket_', or 'scan_'. Overall, still readable and predictable.

Tool Count3/5

33 tools is on the high side for a single server, especially one named 'colorapi' which misleadingly suggests a focus on color only. The tool count is borderline appropriate for the actual broad data integration scope, but the mismatch with the server name is problematic.

Completeness4/5

The tool set covers a wide range of data sources and operations (SEC, FDA, patents, news, Polymarket, memory, subscriptions), with few obvious gaps. Minor lacks: no direct tool for updating stored memories or handling non-US companies, but overall comprehensive.

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