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Glama

Vizier

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

A4.5/5.0
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 scoping detail ('Scoped to your identifier...') which is useful behavioral context beyond annotations. No contradictions.

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-loaded with the core purpose. Every sentence adds unique value: retrieval/listing, usage examples, scoping, and pairing with siblings. No wasted words.

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?

Given one optional parameter, no output schema, and rich annotations, the description fully covers what the tool does, how to use it (including missing key behavior), scoping, and inter-tool relationships. Complete for a simple retrieval tool.

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 coverage is 100% for the single parameter, which already describes 'omit to list all keys'. The description reinforces this usage but adds no new semantic detail 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 specifies a clear verb+resource: 'Retrieve a value previously saved via remember, or list all saved keys'. It distinguishes from sibling tools like remember and forget by explicitly naming 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?

Explicitly states when to use: 'look up context the agent stored earlier' with concrete examples (ticker, address, notes). Advises pairing with remember (save) and forget (delete), providing clear 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

Several tools occupy adjacent roles: ask_pipeworx and ask_pipeworx_beta are currently identical in behavior, discover_tools and suggest_questions both serve discovery, and the astronomy plus Polymarket scanners have overlapping boundaries. The descriptions are unusually detailed and do differentiate most tools, but the number of near-neighbor tools still creates real selection risk.

Naming Consistency3/5

Names are uniformly snake_case and mostly descriptive, which helps, but the grammatical pattern is inconsistent: verb_noun names (compare_entities, resolve_entity) sit alongside bare nouns (catalogs, object) and bare verbs (remember, recall, forget). It is readable but not a predictable verb_noun convention.

Tool Count2/5

At 35 tools, the surface is well beyond what an agent can comfortably hold in mind. The set mixes a data-research core with one-off utilities like generate_llms_txt, scan_dependency, and AI-visibility auditing, making it feel like a grab-bag rather than a scoped server.

Completeness4/5

The core data-research workflow is strongly covered: plain and grounded Q&A, deep research, entity resolution, profiles, comparisons, claim validation, recent changes, tool discovery, memory, and subscription lifecycle all exist. Minor gaps remain, such as no subscription-update operation and no generic citation-fetch tool, but there are no serious dead ends.

Resources