Skip to main content
Glama

Gaze and think (flow session)

scratchbook_gaze

Write hard problem fragments to a board, then launch an isolated background session to reason over their relationships and synthesize a result. Poll or wait for the synthesis.

Instructions

gaze-and-think: write the hard, tangled parts of a problem into a BOARD (a notebook), then launch a CLEAN, isolated flow session — a fresh claude -p that carries no other context and concentrates SOLELY on that board — to reason about the RELATIONSHIPS between the pieces, write intermediate artifacts you can inspect/verify, and converge on a synthesis. Use it when a problem is too tangled to hold in the working session: dump the pieces with note=…, then focus a flow session on them with focus=…. The flow session runs in the BACKGROUND — poll with scratchbook_gaze_read; pass wait=true to block for the synthesis. Give project= to let it ground its thinking in real code. It can read/write files and search the codebase, but has no shell.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
noteNoA piece of material to append to the board's notebook first.
waitNoBlock until the flow session finishes and return its synthesis (bounded by timeout_s). Default false — runs in the background.
boardYesBoard name — a persistent thinking workspace; reuse it to accumulate material.
focusNoThe relationship/question to concentrate on. When given, launches a flow session over the whole board.
modelNoModel for the flow session (default: the configured claude -p model).
titleNoOptional heading for the note.
projectNoAbsolute codebase root the flow session may search to ground claims in real code.
timeout_sNoMax seconds for the flow session (default 900).
Behavior5/5

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

With no annotations provided, the description carries the full burden and does so thoroughly. It discloses that the flow session is 'CLEAN, isolated', 'runs in the BACKGROUND', 'can read/write files and search the codebase, but has no shell', and explains the wait=true blocking behavior.

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 dense but every sentence earns its place: it explains what, when, how, and the operational caveats. It is front-loaded with the tool's core purpose and structured to flow from action to use case to background behavior.

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?

Given the tool's complexity (8 parameters, no output schema, no annotations), the description is rich enough to support correct selection and invocation. It covers the workflow, background vs. blocking behavior, and capabilities/restrictions, though it does not specify the exact structure of the returned synthesis.

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 coverage is 100%, so the baseline is 3. The description adds practical usage context for key parameters (note=, focus=, project=, wait=true), including the pattern of dumping pieces then focusing a session, going beyond raw schema text.

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's function: write tangled problem pieces to a board, then launch an isolated flow session to reason about relationships. It distinguishes itself from the sibling scratchbook_gaze_read by explicitly referring to polling/blocking behavior.

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?

It gives explicit guidance: 'Use it when a problem is too tangled to hold in the working session' and explains the workflow with note=..., focus=..., and wait=true. It clearly points to scratchbook_gaze_read for polling, but does not explicitly discuss when not to use it relative to all other sibling tools.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/jinyaolin/scratchbook'

If you have feedback or need assistance with the MCP directory API, please join our Discord server