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Glama

Pipeworx

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)

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. The description adds valuable context: scoping to user identifier, listing all keys when key omitted, and examples of stored data. No contradiction 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?

Two sentences, no wasted words. Front-loaded with the core action, then provides context and pairing. Highly efficient.

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 read tool with one optional parameter and good annotations, the description covers usage, scoping, and pairing. No output schema, but return values are implied. Minor gap: no explicit mention of what happens if key not found (returns null/error).

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 one optional key parameter. Description explains the effect of omitting the key (list all keys) and the purpose of the parameter, adding meaning 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 retrieves a value saved via remember or lists all keys, with specific examples of use cases (ticker, address, research notes). It distinguishes from sibling tools 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?

Explicitly states when to use (look up stored context) and pairs with remember/forget. Mentions scoping by identifier. Could be improved by clarifying when not to use, but it's sufficient.

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

All Pipeworx toolsInspect
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TDQS

A4/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, especially the central data access tools like ask_pipeworx, deep_research, entity_profile, and compare_entities. However, the multiple polymarket tools (arbitrage, edges, edge_tracker, fill_risk, kalshi_spread) and the similar ask_pipeworx variants could cause confusion, especially for an agent quickly scanning options.

Naming Consistency4/5

Tool names are mostly snake_case and follow a verb_noun pattern (e.g., compare_entities, search_packs, resolve_entity). Some deviations exist, such as pipeworx_feedback, polymarket_arbitrage (starting with a noun), and single-word names like forget and remember, but overall the style is readable and consistent.

Tool Count2/5

With 36 tools, the server feels overly heavy. While the broad domain (structured data across many sources) justifies a large number, the count exceeds the recommended 15–25 range, making it unwieldy for agents to navigate efficiently without extensive discovery.

Completeness4/5

The tool set covers a wide range of domains: company financials, drugs, economics, prediction markets, weather, and even MCP discovery. There are few obvious gaps given the stated purpose, though some areas like social media or international data could be added. Overall, the surface is well-rounded.

Resources