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Glama

Paperswithcode

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. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already indicate read-only and idempotent. The description adds valuable context: scoping to identifier and effect of omitting key (list all). This goes beyond what annotations provide.

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?

Two sentences, front-loaded with main function, followed by usage examples. No fluff, every sentence earns its place.

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 read-only lookup with one optional parameter and no output schema, the description covers purpose, usage, scoping, and sibling relationships completely.

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 parameter description. The description reinforces that omitting key lists all, but adds minimal meaning beyond the schema. Baseline of 3 is appropriate.

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 'Retrieve a value previously saved via remember, or list all saved keys' with specific verb (retrieve/list) and resource (memory). It distinguishes from siblings remember (save) and forget (delete).

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?

Explicitly says 'Use to look up context the agent stored earlier' and provides examples of use cases. It also directs to pair with remember to save, forget to delete, giving clear when-to-use and 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

A3.8/5.0
Disambiguation2/5

Several tools form overlapping families: ask_pipeworx and ask_pipeworx_beta are currently identical, ask_pipeworx_grounded and deep_research cover much of the same router territory, and five Polymarket tools overlap heavily on edge/arbitrage analysis. The descriptions are detailed, but an agent would regularly need to compare multiple near-equivalent candidates before choosing one.

Naming Consistency3/5

Names are consistently snake_case and readable, but the conventions vary widely: verb_noun (search_papers, resolve_entity), noun phrases (entity_profile, polymarket_arbitrage, recent_changes), bare verbs (remember, subscribe, forget), and suffixed variants (ask_pipeworx_beta, ask_pipeworx_grounded). It is not chaotic, but there is no single predictable pattern.

Tool Count2/5

Thirty-five tools is far too many for a server named Paperswithcode, especially since only four tools actually relate to papers while the rest cover data routing, prediction markets, memory, subscriptions, npm auditing, llms.txt generation, and AI visibility. The count feels bloated and the scope unfocused relative to the server's apparent identity.

Completeness3/5

The paper-discovery subdomain is reasonably covered with search, trending, detail, and implementation lookup, and the broader set includes discovery, memory, subscription lifecycle, and feedback tools. However, the overall surface is a patchwork of unrelated domains with no well-defined boundary, and paper datasets/models can only be counted rather than directly listed. Agents can work around most gaps, but the coverage is uneven.

Resources