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Glama

Prh Fi

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

A4.2/5.0
Behavior4/5

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

Annotations show write operation, idempotent, non-destructive. Description adds value by specifying scoping by identifier and persistence differences between authenticated users and anonymous sessions.

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?

Four sentences, front-loaded with main purpose, every sentence adds value. No wasted words.

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?

Adequate for a simple write tool with no output schema. Covers when, what, scoping, and durability. No return value explanation needed.

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 covers both parameters with descriptions (100% coverage). Description does not add additional parameter-level meaning beyond the schema, meeting baseline.

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 'Save data the agent will need to reuse later' with a specific verb and resource. It distinguishes from siblings like recall and forget by mentioning they are paired.

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 says 'Use when you discover something worth carrying forward' and provides examples. It mentions pairing with recall and forget but does not include when-not-to-use guidelines.

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

A3.7/5.0
Disambiguation2/5

Many tools have overlapping purposes, e.g., ask_pipeworx, ask_pipeworx_grounded, deep_research, entity_profile, and compare_entities all retrieve factual data about entities. Additionally, multiple polymarket tools (bet_research, polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread) are highly specialized but can be confused without careful reading of descriptions.

Naming Consistency1/5

Naming conventions are highly inconsistent: snake_case (ai_visibility_check, generate_llms_txt), lowercase phrases (ask_pipeworx, forget, recall), and mixed styles (entity_profile, scan_competitor_ai_presence). No clear pattern like verb_noun; tools like 'remember' and 'forget' are single words while others are lengthy phrases.

Tool Count3/5

32 tools is on the high side for a single server, but the coverage is broad (data retrieval, prediction markets, Finnish registry, memory, utilities). However, many tools are redundant or overly specialized, making the count feel bloated. A more focused set could be 20-25 tools.

Completeness3/5

The Pipeworx tools provide deep coverage for factual data retrieval, and the Finnish registry has basic operations. However, there are gaps in other areas, and the set lacks a unified domain. The inclusion of memory tools and a random llms.txt generator feels out of place, breaking the coherence.

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