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Glama

Smithsonian

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

A4.3/5.0
Behavior4/5

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

Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds context about scoping to user identifier and the listing behavior when key is omitted, which goes 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?

The description is three sentences with zero waste. The main action is front-loaded, and every sentence adds necessary context without redundancy.

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?

Given one optional parameter, no output schema, and thorough annotations, the description fully covers behavior, scoping, and sibling relationships. It is complete for an agent to decide and invoke correctly.

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 coverage is 100%, and the description repeats the parameter behavior (omit to list all keys) already present in the schema. It adds no new semantic value for the parameter beyond what the schema provides.

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 clearly states the tool retrieves a saved value or lists all keys, with specific verb 'Retrieve' and resource 'value previously saved via remember'. It distinguishes itself from siblings by mentioning pairings with 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?

The description gives concrete examples of when to use (look up target ticker, address, research notes) and mentions scoping. It explicitly references alternative tools (remember, forget) but does not formally state when not to use.

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

All Smithsonian toolsInspect
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TDQS

A3.9/5.0
Disambiguation3/5

Many tools serve overlapping purposes, such as the three ask_pipeworx variants (stable, beta, grounded) and the six Polymarket-specific tools. While detailed descriptions help distinguish them, an agent could still confuse bet_research with polymarket_edges or the ask_pipeworx versions.

Naming Consistency2/5

Naming is highly inconsistent: verb_noun (ask_pipeworx, compare_entities), noun_noun (bet_research, entity_profile), single verbs (forget, recall, search), and adjective_noun (recent_alerts, deep_research). No clear pattern emerges across the set.

Tool Count3/5

33 tools is on the high side for a server named 'Smithsonian' that actually covers a broad range of data sources (SEC, FDA, Polymarket, npm, etc.). The number feels borderline heavy but is still manageable if the server's true purpose is general research.

Completeness4/5

The tool set covers multiple domains (company financials, drugs, economics, prediction markets, npm, museum data) with reasonable depth. Minor gaps exist, such as lack of PyPI scanning or missing update/delete operations for some memory features, but core workflows are well-supported.

Resources