Skip to main content
Glama

Science Feeds

by io.github.pipeworx-io

Remember

remember
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. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare idempotentHint=true and destructiveHint=false. The description adds context about scope ('scoped by your identifier'), expiration (24 hours for anonymous), and pairing with recall/forget, 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.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Multiple sentences but each adds value. Front-loaded with purpose. No redundant information. Could be slightly more concise, but structure (purpose, usage, mechanics, pairing) is logical.

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?

No output schema, but for a write tool this is acceptable. Covers purpose, usage, persistence, and pairing with siblings. Adequately complete for an agent to use correctly.

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% with descriptions. The description adds value by giving example keys ('subject_property', 'target_ticker') and clarifying value semantics ('any text'), enhancing understanding beyond the schema.

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's purpose ('Save data the agent will need to reuse later') and resource ('key-value pair'). It distinguishes itself from sibling tools like recall and forget by explicitly pairing with 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?

Provides explicit guidance on when to use ('when you discover something worth carrying forward'), references alternatives (recall, forget), and notes authentication-based persistence differences.

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

All Science Feeds toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.7/5.0
Disambiguation2/5

Several tools have overlapping purposes: ask_pipeworx, ask_pipeworx_beta (currently identical), ask_pipeworx_grounded, deep_research, and validate_claim all route questions to data, while the six polymarket_* tools blur edge-finding, arbitrage, and fill-risk. ai_visibility_check and scan_competitor_ai_presence also overlap, with the latter wrapping the former.

Naming Consistency3/5

All names are snake_case and readable, with clear families like polymarket_*, ask_pipeworx*, and list_*. However, conventions mix verb-first names (resolve_entity, read_feed) with noun-first names (entity_profile, pipeworx_trending, recent_alerts, deep_research), so no single pattern dominates.

Tool Count2/5

34 tools is heavy for any server, but the real issue is scope sprawl: science feeds, a universal data router, prediction-market analysis, memory, subscriptions, AI-visibility checks, and npm scanning each form a mini-server. The count is not defensible for the 'Science Feeds' purpose and would be better split into several focused servers.

Completeness2/5

There is no coherent domain to assess completeness against—'Science Feeds' describes only 3 of 34 tools. Individual clusters are partially complete (subscriptions have subscribe/list/unsubscribe/recent_alerts, memory has remember/recall/forget), but the overall surface is a grab-bag of features from unrelated products, with obvious gaps in each (e.g., no way to update a subscription, no direct access to specific data packs except through the router).

Resources