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Glama

Codestats

by io.github.pipeworx-io

Remember

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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. First observed

TDQS

A4.7/5.0
Behavior5/5

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

Annotations provide idempotentHint=true and destructiveHint=false, but the description adds critical behavior beyond annotations: 'scoped by your identifier,' persistence differences between authenticated (persistent) and anonymous (24 hours) sessions. It also notes the key-value storage format. No contradiction with annotations; description enriches the agent's understanding of side effects.

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?

The description is three sentences, front-loaded with the primary purpose, followed by usage context, and ends with storage semantics and sibling references. Every sentence adds value with no redundancy or filler.

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?

The tool is simple (2 parameters, no output schema), and the description covers purpose, when to use, persistence behavior, and related tools. This is fully sufficient for an agent to select and invoke the tool correctly with no missing critical information.

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 description coverage is 100%, with clear explanations for both 'key' (e.g., 'subject_property') and 'value' (any text). The description reinforces key-value pairing but does not add new parameter-specific details beyond what the schema already provides. Hence, baseline 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 the tool's purpose: 'Save data the agent will need to reuse later' with a specific verb and resource (key-value store). It further differentiates from siblings by explicitly naming recall (retrieve) and forget (delete), making its role in the memory system unambiguous.

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?

The description provides explicit usage guidance: 'Use when you discover something worth carrying forward' with examples like resolved ticker or user preference. It also clarifies the alternative path by instructing to pair with recall for retrieval and forget for deletion, effectively distinguishing from sibling tools.

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

A3.5/5.0
Disambiguation3/5

Most tools have distinct, well-described purposes, but clusters like ask_pipeworx/ask_pipeworx_beta/ask_pipeworx_grounded and the five polymarket_* tools have overlapping scopes that could cause misselection. The beta currently behaves identically to the stable router, and `recent` vs `recent_changes` vs `recent_alerts` are confusingly similar names for different domains.

Naming Consistency2/5

All names use lowercase underscores, but there is no consistent verb_noun pattern: some are verbs (ask, compare, generate), some are nouns (entity_profile, recent, user), and some are adjectives (deep_research). There are coherent subfamilies (subscribe/unsubscribe/list_subscriptions, remember/recall/forget), but the overall naming is a mix of conventions.

Tool Count2/5

33 tools is well above the 25+ threshold, making the surface heavy and hard to navigate. Many are highly specialized meta-tools (e.g., five Polymarket analyzers) that could be consolidated.

Completeness2/5

For a server named 'Codestats', only `recent` and `user` address coding stats, leaving major gaps in what that name implies. The broader data/research/betting capabilities are fairly rich, but the server's stated identity is under-served and there's no clear lifecycle coverage for any single domain.

Resources