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Glama

Kegg

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

A4.4/5.0
Behavior5/5

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

Adds scoping context (anonymous IP, BYO key hash, account ID) beyond annotations. No contradictions with readOnlyHint, idempotentHint, destructiveHint.

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?

Every sentence adds value: two modes, usage examples, scoping, pairing with other tools. No wasted words, front-loaded with main action.

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?

Given no output schema, description could mention return format (e.g., string value or array of keys). But it's complete enough for its simplicity; minor gap.

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 already describes the 'key' parameter with 100% coverage. Description adds that omitting key lists all keys, matching schema's 'omit to list all keys'. Baseline 3 is appropriate; description provides marginal extra context.

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 the tool retrieves a saved value by key or lists all keys when key is omitted. Distinguishes from siblings like 'remember' and 'forget'.

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?

Explicitly says when to use: to look up previously stored context without re-deriving. Gives examples like target ticker, address. Pairs with remember/forget. Could be more explicit about when not to use but still strong.

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

All Kegg toolsInspect
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TDQS

A3.8/5.0
Disambiguation3/5

Most tools have clearly described distinct purposes, but several overlap: ask_pipeworx and ask_pipeworx_beta are explicitly identical right now, and ask_pipeworx_grounded, deep_research, discover_tools, and suggest_questions all occupy neighboring query/discovery territory. The Polymarket and memory tool families, by contrast, are well differentiated.

Naming Consistency3/5

All names are snake_case and readable, but the set mixes verb-first names (ask_pipeworx, compare_entities, resolve_entity, validate_claim) with noun-phrase names (entity_profile, bet_research, recent_alerts, polymarket_arbitrage) and brand prefixes (pipeworx_*, polymarket_*). There is no consistent verb_noun pattern across the toolkit.

Tool Count2/5

34 tools is well beyond the heavy range, and the count is especially inappropriate because the server is named Kegg but only find, get_entry, and list_database relate to KEGG bioinformatics. The remaining 31 tools span unrelated domains (generic data research, prediction markets, memory, subscriptions, AI visibility, npm scanning), making the scope feel like several products merged into one.

Completeness2/5

As a KEGG server, the surface is severely thin: three read-only tools with no pathway mapping, sequence search, or cross-reference utilities. The Pipeworx research and Polymarket betting subsystems are more complete, but their presence under a Kegg server makes the overall surface incoherent rather than complete.

Resources