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science

by io.github.pipeworx-io

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Idempotent

Save data the agent will need to reuse later — across this conversation or across sessions. Use when you discover something worth carrying forward (a resolved ticker, a target address, a user preference, a research subject) so you don't have to look it up again. Stored as a key-value pair scoped by your identifier. Authenticated users get persistent memory; anonymous sessions retain memory for 24 hours. Pair with recall to retrieve later, forget to delete.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keyYesMemory key (e.g., "subject_property", "target_ticker", "user_preference")
valueYesValue to store (any text — findings, addresses, preferences, notes)

Schema Changelog

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

  1. Added

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already indicate this is a non-read-only, idempotent, non-destructive write. The description adds valuable context beyond annotations: key-value scoping per identifier, persistence differences between authenticated and anonymous sessions, and the 24-hour retention for anonymous sessions. This extra behavioral detail justifies above baseline.

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 deliver purpose, usage context, storage semantics, and lifecycle pairing with zero redundancy. The most important information is front-loaded in the first sentence.

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 two-parameter key-value set operation, the description covers purpose, when to use, storage scoping, persistence behavior, and how it fits with related tools. No output schema is needed since the operation is a write, and no other behavioral details are essential.

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% with descriptive parameter details for both 'key' and 'value' (including examples and allowed content). The description merely reinforces that storage is key-value without adding new semantic meaning, so baseline 3 applies.

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 and resource ('Save data the agent will need to reuse later'), clearly identifying the tool's function. It distinguishes from siblings by explicitly pairing with 'recall' and 'forget' and clarifies the key-value storage model.

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?

It provides explicit when-to-use guidance with concrete examples ('a resolved ticker, a target address, a user preference, a research subject') and tells the agent to use it when avoiding redundant lookups. It also names the relevant sibling tools for retrieval and deletion, giving clear alternatives.

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.

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