Skip to main content
Glama

save_finding

Save critical research findings, insights, and discoveries to a persistent library. Automatically detects duplicates to keep your knowledge base organized and accessible.

Instructions

Save critical research findings, discoveries, and insights to persistent library.

AUTOMATIC TRIGGERS - Call this when:

  • You discover important factual information during research

  • You complete an analysis with actionable insights

  • You find evidence supporting or refuting a hypothesis

  • You learn something new about the codebase or project

  • User shares important information that should be remembered

DO NOT CALL for:

  • Temporary working notes

  • Process updates or progress reports

  • Conversation summaries

  • Tool execution results (unless they contain novel insights)

WORKFLOW POSITION: Call after discovering insights, before ending session. Save findings as you go - don't wait until the end.

READ-BEFORE-WRITE: This tool automatically checks for similar findings before saving. If a very similar finding exists (similarity > 0.90), it will be returned as a suggestion instead of saving a duplicate. Consider updating the existing finding instead.

SESSION AWARENESS: For best results, use start_research or session_start before saving findings. This enables automatic context injection and session-based knowledge tracking. If no active session is detected, a warning will be returned.

CONFIRMATION TIER: WRITE OPERATION - Requires confirm=True. This creates persistent data. Set confirm=true for final saves, confirm=false for drafts.

PARAMETERS:

  • project: Project name for categorization (required)

  • claim: The finding/insight text (required) - be specific and actionable

  • confidence: Confidence level 0.0-1.0 (default: 0.8). Use 0.9 for definitive findings, 0.7 for tentative, 0.5 for hypotheses

  • evidence: Supporting evidence strings (optional) - quotes, data points, references

  • reasoning: Your reasoning behind the finding (optional but recommended)

  • caveats: Limitations or cave (optional)

  • tags: Tags for categorization (optional) - use consistent tags

  • confirm: Must be True to save (safety gate). Use False for drafts.

TIP: If a similar finding already exists, consider updating it instead of creating a duplicate. Use search_findings first to check for duplicates.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tagsNo
claimYes
caveatsNo
confirmNo
projectYes
evidenceNo
full_textNo
reasoningNo
confidenceNo
finding_idNo
proposed_byNo
Behavior5/5

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

With no annotations provided, the description carries full responsibility and does so thoroughly. It discloses the duplicate-check behavior with a similarity threshold, the confirmation requirement, session awareness warnings, and that this creates persistent data. These are meaningful behavioral traits beyond the input schema.

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 long but exceptionally well organized into labeled sections: automatic triggers, exclusions, workflow, read-before-write, session awareness, confirmation tier, and parameters. Each section adds necessary information without filler, and the most important usage guidance 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?

For a complex 11-parameter write tool with no annotations and no output schema, the description covers triggers, exclusions, parameter semantics, duplicate handling, confirmation, and session dependencies. It falls slightly short by omitting three parameters and not specifying the success response shape, but overall it is nearly complete.

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 description coverage is 0%, so the description is the only source of parameter meaning. It explains most parameters well with usage guidance (confidence scale, confirm safety gate, evidence/reasoning/caveats purpose). However, it omits full_text, finding_id, and proposed_by, leaving their semantics undocumented, which keeps this from a 5.

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 critical research findings, discoveries, and insights to persistent library.' It clearly differentiates from siblings by listing automatic triggers and explicit DO NOT CALL cases, so an agent knows this is for persistent insight storage rather than temporary notes or tool outputs.

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?

The description provides explicit when-to-call triggers, a DO NOT CALL list, workflow positioning, and a tip to use search_findings first for deduplication. This gives strong, unambiguous guidance on when to invoke this tool versus alternatives.

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/Vedant9500/OpenLMlib'

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