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Glama

Passive Aggression

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 declare readOnlyHint=true and idempotentHint=true. The description adds context about scoping (anonymous IP, BYO key hash, account ID) and pairing with remember/forget, without contradicting 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 sentences front-load the primary function, then provide examples, use case, and scoping. Every sentence adds value with no waste.

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 tool with one optional parameter, good annotations, and no output schema, the description fully covers purpose, usage, and constraints.

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% for the single parameter 'key'. The description adds the meaning of omitting it to list all keys, which goes beyond the schema's 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 states 'Retrieve a value previously saved via remember, or list all saved keys', which is a specific verb+resource combination. It clearly distinguishes from siblings like 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?

It explicitly states when to use: 'to look up context the agent stored earlier', and implies when not to use by noting 'without re-deriving it from scratch'. It also mentions scoping and pairing with other tools.

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

All Passive Aggression toolsInspect
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TDQS

A3.8/5.0
Disambiguation2/5

Multiple tools have overlapping purposes, particularly the various data query tools (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, bet_research, etc.) that share similar functionality with subtle differences. The detailed descriptions help but the boundaries are not always clear, making it hard for an agent to reliably select the correct tool.

Naming Consistency3/5

The naming follows a mix of patterns: some tools use consistent verb_noun (subscribe, unsubscribe, remember, recall) but others are inconsistent (ask_pipeworx vs deep_research vs entity_profile). The main data tools have a common prefix but diverge in style, and the presence of tools like passive_aggression_detect and generate_llms_txt adds further inconsistency.

Tool Count2/5

With 32 tools, the server feels bloated. Many tools could be consolidated (e.g., the ask_pipeworx variants, the Polymarket tools). The inclusion of tangential tools like passive_aggression_detect and generate_llms_txt suggests scope creep. A typical well-scoped server would have 10-15 tools.

Completeness3/5

The server covers a wide range of data access and some auxiliary functions (memory, subscriptions, feedback), but there are notable gaps like user authentication and data visualization. The addition of an unrelated sentiment analysis tool makes the surface feel incomplete for a focused data server.

Resources