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Glama

uluesky

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. Added

TDQS

A4.9/5.0
Behavior5/5

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

The description adds valuable behavioral context beyond annotations: memory is key-value scoped by identifier, persistent for authenticated users, and retained for 24 hours for anonymous sessions. No contradiction with provided 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 tightly-worded sentences, each earning its place: main purpose, usage trigger with examples, and persistence details. No fluff or repetition.

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 key-value store with two parameters and no output schema, the description provides complete guidance on purpose, usage, persistence, and companion tools. It covers all essential aspects without needing an output schema.

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 already fully documents both parameters with examples, so baseline is 3. The description adds the key-value pairing concept and scoping by identifier, slightly enhancing understanding of how parameters relate.

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 saves data for later reuse across conversations or sessions, using the specific verb 'save' and resource 'data'. It distinguishes itself from sibling tools recall and forget by explicitly pairing with them.

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?

It provides explicit when-to-use guidance with concrete examples (resolved ticker, target address, user preference) and tells the agent to pair with recall and forget for retrieval and deletion. This clearly contrasts with 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

A3.6/5.0
Disambiguation3/5

Most tools have clearly distinct roles, but several overlapping pairs create ambiguity: ask_pipeworx vs ask_pipeworx_beta are explicitly identical today, discover_tools vs suggest_questions both serve discovery/onboarding, and bet_research vs polymarket_edges both address betting-edge questions. The detailed descriptions help, but an agent could still select the wrong tool in these cases.

Naming Consistency2/5

The set uses at least four naming conventions: get_* for Bluesky reads, verb_noun for Pipeworx tools (ask_pipeworx, resolve_entity, validate_claim), polymarket_* prefixed tools, and verb-only memory tools (remember, recall, forget). Each subgroup is internally consistent, but the overall mix feels inconsistent and unpredictable.

Tool Count2/5

39 tools is well beyond the typical well-scoped server, and the scope sprawls across Bluesky reads, Pipeworx data, Polymarket analysis, memory, subscriptions, and one-off utilities like generate_llms_txt and scan_dependency. The count would be more reasonable split into separate servers; as-is it feels heavy and unfocused.

Completeness4/5

Within the server's evident scope, coverage is strong: Bluesky read operations, Pipeworx query/research/verification, entity profiling, and subscription lifecycle are all represented. The main gaps are write actions for Bluesky (posting, following, liking) and a few auxiliary features that are only partially integrated, but no critical workflow dead-ends appear for the primary data-research use cases.

Resources