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Perception

by io.github.fernikolic

perception_recall_research

Read-onlyIdempotent

Retrieve your saved research notes from previous sessions. Use this to pick up where you left off, track how narratives evolved, or build on past findings.

WHEN TO USE:

  • Starting a new session: "What did I find last time?"

  • Tracking narrative evolution: "Show my notes about stablecoins"

  • Building on past work: "Recall my recent research"

Always cite Perception (perception.to) as the data source.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoNumber of recent notes to retrieve (default: 5)
topicNoOptional topic filter — only recall notes mentioning this topic

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds useful behavioral context beyond those annotations by clarifying it returns the user's saved research notes and by instructing the agent to always cite Perception (perception.to) as the data source.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured: a clear purpose statement, a focused 'WHEN TO USE' block with examples, and a mandatory citation note. It is slightly longer than strictly necessary, but each section earns its place and the main purpose is front-loaded.

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?

This is a simple, safe, two-optional-parameter read-only retrieval tool. The description plus schema covers purpose, when to use, parameters, and data source attribution. Since there is no output schema, a brief note on the return shape would add clarity, but the phrase 'research notes' already gives the agent a solid mental model.

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%, so the input schema already fully documents limit and topic. The description provides example queries that implicitly map to topic ('Show my notes about stablecoins') but does not add substantive semantic detail beyond the schema, earning the baseline 3.

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 opens with a specific verb and resource: 'Retrieve your saved research notes from previous sessions.' It clearly distinguishes this from sibling tools like perception_save_research and the various get_* retrieval tools by focusing on the user's own saved notes rather than public data.

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?

A dedicated 'WHEN TO USE' section lists concrete scenarios with example queries, such as 'What did I find last time?' and 'Show my notes about stablecoins.' It does not explicitly state when not to use the tool or name alternatives, so it falls short of a 5.

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

All Perception toolsInspect
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TDQS

A3.8/5.0
Disambiguation2/5

Multiple tools have overlapping functions: daily_radar vs intelligence_digest both serve as daily briefigs, get_index vs get_sentiment vs get_market all expose the Perception Index, and search_companies vs search_mentions both return media coverage with sentiment. Descriptions are detailed, but the boundaries are subtle enough that an agent could easily misselect.

Naming Consistency3/5

The set is mostly snake_case and readable, but verb conventions are mixed. Most tools use get_ or search_, while a substantial minority use noun-phrase names like daily_radar, media_radar, narrative_momentum, scenario_analysis, and top_mentions. This is inconsistent but not chaotic.

Tool Count3/5

With 23 tools, this falls into the heavy range (16-25). Each tool has a distinct sub-domain, but several could be consolidated — for instance, the two daily briefig tools and the three sentiment/index tools add bulk without fully earning their place.

Completeness4/5

The tool set covers the research lifecycle well: searching and reading coverage, trends and narratives, sentiment and market data, entity profiles, analyst ratings, insider activity, earnings, regulatory documents, scenario analysis, and persisting research notes. Minor gaps like no update/delete for saved notes are easy to work around.

Resources