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Glama

science

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. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "key": "user_research_topic"
+  },
+  {}
+]
  2. Added

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, and non-destructive behavior. The description adds valuable context: scoping to user identifier (anonymous IP, BYO key hash, or account ID) and the behavior of omitting the key to list all keys. No contradictions with 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 concise sentences, front-loaded with the primary purpose, followed by usage context and scoping. No redundant phrases; every sentence adds value.

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?

For a simple one-parameter tool with no output schema, the description covers return behavior (value or list of keys), scoping, and relationship to sibling tools. It does not explicitly state error behavior (e.g., key not found), but that is not critical for completeness at this level.

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%, so baseline is 3. The description adds meaning by explaining that the key refers to a value saved via remember, provides concrete examples (user_research_topic, address), and clarifies that omitting the key lists all keys—beyond the schema's brief description.

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 a specific verb+resource ('Retrieve a value previously saved via remember') and clearly distinguishes the tool's two modes (retrieve by key or list all keys). It also differentiates from sibling tools like remember and forget by identifying the action as retrieval.

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 explicit context for when to use (look up previously saved context like target ticker or research notes) and pairs with remember/forget. It lacks an explicit 'when not to use' clause, but the guidance is clear and practical.

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

Several tools have overlapping purposes, such as ask_pipeworx, ask_pipeworx_grounded, and deep_research all answering questions with slight differences. Also, entity_profile and compare_entities both retrieve company data, and the multitude of Polymarket tools can be confusing. However, many tools have distinct use-cases, so the ambiguity is moderate.

Naming Consistency3/5

Tool names mix consistent patterns (e.g., get_air_quality, get_apod) with less predictable ones (e.g., pipeworx_feedback, polymarket_arbitrage, bet_research). Some follow verb_noun, others are noun_verb or just noun. The inconsistency is noticeable but not chaotic.

Tool Count2/5

With 34 tools, the server feels overloaded for a 'science' domain. Many tools are dedicated to prediction markets (Polymarket) and finance, which seem tangential. The count could be reduced by merging similar query tools or removing domain-specific betting tools to better fit the scientific theme.

Completeness2/5

While the server covers a broad range of data sources (SEC, FDA, FRED, etc.), it lacks core scientific tools for physics, chemistry, or biology. The few science-themed tools (get_apod, get_earthquakes) are minor. The set feels incomplete for a dedicated science server, with emphasis on finance and betting instead.

Resources