Geond Agent Protocol

Get workspace lineage graph

get_workspace_lineage_graph

Map collaboration artifacts in a workspace by returning a graph of sessions, changesets, handoffs, and reservations. Use the output for dashboard and orchestration analysis.

Instructions

Purpose: Return a graph of major collaboration artifacts for a workspace. When to use: call when an agent needs a high-level map of sessions, changesets, handoffs, reservations, and activity. Inputs: workspace_id and limit. Side effects: none beyond database reads. Output: nodes and edges suitable for dashboard or orchestration analysis. Failure modes: returns a sparse graph for new or unindexed workspaces.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "workspace_id": "<workspace_id>"
    },
    "name": "get_workspace_lineage_graph"
  }
}

Input Schema

Name	Required	Description	Default
`limit`	No	Maximum number of lineage nodes or rows to include.
`workspace_id`	Yes	Workspace UUID whose lineage graph should be returned.

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Jun 3, 2026

TDQS

A4.5/5.0

Behavior5/5

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

No annotations are provided, so the description fully carries the burden. It discloses no side effects beyond reads, mentions failure modes (sparse graph for new workspaces), and implies read-only behavior. This is comprehensive for a graph-returning tool.

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?

Extremely concise with bullet-like 'Purpose', 'When to use', 'Inputs', 'Side effects', 'Output', 'Failure modes'. No fluff, easy to scan, and each section serves a clear purpose.

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 the tool's complexity (graph output), the description covers inputs, side effects, output format, and failure modes. Output schema exists so return values are defined elsewhere. No gaps remain.

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 baseline is 3. Description merely lists parameters without adding new semantics beyond what the schema already provides. No format constraints or interaction notes are given.

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?

Description clearly states it returns a graph of major collaboration artifacts for a workspace. The verb 'return a graph' is specific and the resource 'major collaboration artifacts' is well-defined. It distinguishes from siblings like get_active_reservations or get_dashboard_overview which focus on specific items rather than a holistic graph.

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?

Explicitly states when to use: when a high-level map is needed. It does not explicitly exclude scenarios or name alternatives, but the purpose is clear enough for an agent to infer appropriate usage. Sibling tools exist for more specific queries, but no contrast is provided.

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