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Perception

by io.github.fernikolic

perception_save_research

Save a research note from your current session. Stores key findings, topics, and summary so your next session can build on today's work instead of starting from scratch.

WHEN TO USE:

  • End of a research session: "Save this briefing for tomorrow"

  • After a deep dive: "Remember these findings"

  • When you want to track how a narrative evolves over multiple sessions

Notes are stored for the authenticated account or team and persist across conversations. Use perception_recall_research to retrieve them later.

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

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
titleYesBrief title for this research note (e.g., 'Stablecoin regulation deep dive', 'Morning briefing Mar 22')
topicsNoTopics covered (e.g., ['stablecoin', 'regulation', 'Circle'])
summaryYesKey findings summary — what was learned
key_findingsNoBullet-point key findings to remember for next session

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already indicate readOnlyHint=false and destructiveHint=false, so the basic write behavior is known. The description adds meaningful context beyond that by noting notes persist across conversations, are stored for the authenticated account or team, and that the source must be cited. It does not explain potential duplicate behavior or return outcomes, but the annotation coverage lowers the burden.

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 front-loaded with the core purpose, then organized into a WHEN TO USE list, persistence note, and retrieval pointer. Every section serves a distinct selection or invocation purpose, and there is no fluff or repetition of schema details.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers purpose, persistence, and related tools well, but because there is no output schema, it leaves unclear what happens on success (e.g., whether a note ID is returned) and whether repeated saves create duplicates or overwrite. These gaps are minor for a simple write tool but still exist.

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%, and each parameter already has a clear description with examples. The tool description mentions 'key findings, topics, and summary' but adds no extra parameter-level semantics beyond the schema, so the baseline of 3 applies.

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: 'Save a research note from your current session.' It clearly states that the tool stores key findings, topics, and summary for future sessions, and it distinguishes itself from the sibling tool by naming perception_recall_research as the retrieval counterpart.

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?

A dedicated 'WHEN TO USE' section provides concrete scenarios with example user utterances, such as saving a briefing at the end of a session or remembering findings after a deep dive. It also explicitly directs the agent to use perception_recall_research for retrieval, making the selection criteria unambiguous.

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

A3.8/5.0
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