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Glama

Skiddle

by io.github.pipeworx-io

Recall

recall
Read-onlyIdempotent

Retrieve a value previously saved via remember, or list all saved keys (omit the key argument). Use to look up context the agent stored earlier — the user's target ticker, an address, prior research notes — without re-deriving it from scratch. Scoped to your identifier (anonymous IP, BYO key hash, or account ID). Pair with remember to save, forget to delete.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keyNoMemory key to retrieve (omit to list all keys)

Schema Changelog

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

  1. First observed

TDQS

A4.7/5.0
Behavior4/5

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

Annotations already provide readOnlyHint, destructiveHint, idempotentHint. Description adds scoping context ('Scoped to your identifier (anonymous IP, BYO key hash, or account ID)') which adds behavioral transparency beyond 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?

Three sentences, front-loaded with main action, no wasted words. Every sentence adds value.

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?

For a simple tool with one optional parameter and no output schema, the description is complete: covers purpose, usage guidelines, scoping, and interaction with siblings.

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 coverage is 100% but description adds semantic depth by explaining the optional key parameter behavior ('omit the key argument') and provides concrete use case examples (target ticker, address, research notes).

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?

Description clearly states 'Retrieve a value previously saved via remember, or list all saved keys (omit the key argument).' It distinguishes itself from sibling tools remember and forget by explicitly naming them.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Description explicitly states when to use ('to look up context the agent stored earlier') and when not to need re-derivation. It names alternatives: 'Pair with remember to save, forget to delete.'

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

All Skiddle toolsInspect
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TDQS

A3.9/5.0
Disambiguation3/5

Most tools have clearly distinct roles, but the ask_pipeworx / ask_pipeworx_beta / ask_pipeworx_grounded / deep_research cluster overlaps heavily as question-answering and research entry points, with ask_pipeworx_beta currently being an exact duplicate of ask_pipeworx. The prediction-market tools are individually differentiated but numerous enough that selecting the right one requires careful reading.

Naming Consistency4/5

The set is predominantly snake_case and mostly readable, with sensible prefixes like ask_, search_, polymarket_, and scan_. However, conventions mix verb-first names (remember, subscribe, validate_claim), noun-style names (categories, event, entity_profile), and adjective-noun names (recent_alerts, recent_changes), so the pattern is not fully uniform.

Tool Count2/5

34 tools is well above the 25-tool threshold where a server starts to feel heavy, and many could be consolidated (7+ prediction-market tools, 4+ overlapping ask/research tools, plus memory and subscription helpers). The breadth is somewhat justified by the Pipeworx data platform, but the surface is still overloaded for a single server.

Completeness4/5

For the broad data/research domain, coverage is strong: lookup, grounded verification, deep research, entity profiles, comparisons, change feeds, tool discovery, memory, and subscription lifecycle all have coherent coverage. Minor gaps exist, such as no explicit tool to read a pipeworx:// citation URI directly and a fairly thin Skiddle events side beyond search/detail/categories.

Resources