Skip to main content
Glama
papyruslabs-ai

Seshat

Official

Trace Data Path

trace_data_path
Read-onlyIdempotent

Trace data from a function through its call graph to every sink, listing database writes, network egress, filesystem writes, and tables touched before you edit data-handling code.

Instructions

Follow data from one function through the call graph to its sinks. From a start entity it walks callees, records the data each hop consumes/produces/mutates, and reports the chain from the start to every sink the data reaches — database writes, network egress, filesystem writes — plus the tables touched. Call it before changing a function that handles real data: it answers "where does this data end up?", the cross-call composition get_data_flow (one entity) cannot give. Flags untrusted inputs at the source.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
projectNoProject name (required in multi-project mode). Use list_projects to see available projects.
entity_idYesEntity ID or name to trace data flow from
max_depthNoHow many call hops to follow downstream (default: 5, max: 8)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.20.2

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so safety is covered. The description adds real value beyond that: it explains the traversal mechanism (walks callees hop by hop), what is recorded at each hop, which sink classes are reported, and that untrusted inputs are flagged at the 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?

Front-loaded with the core action and mechanism in the first sentence, then usage and differentiation. Dense but every clause carries information; the only mild excess is the sink enumeration, which is still useful.

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?

No output schema exists, and the description compensates by describing the return shape: the chain from start to every sink, sink categories, and tables touched. Prerequisites are implied by the usage sentence. A brief note on how the chain is ordered or truncated at max_depth would make it fully complete.

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%, so all three parameters are already documented in the schema (including the max_depth default and cap). The description reinforces the start-entity semantics and mentions the tables-touched output but adds no syntax or format detail beyond the schema, so the baseline 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?

States a specific verb and resource ('Follow data from one function through the call graph to its sinks') and precisely delineates scope: walks callees, records consumed/produced/mutated data, reports chains to sinks plus tables touched. It explicitly contrasts with the sibling get_data_flow, so an agent can separate the two without opening either schema.

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?

Gives an explicit when: 'Call it before changing a function that handles real data.' It also names the alternative (get_data_flow) and the exact capability gap that selects trace_data_path over it ('the cross-call composition get_data_flow (one entity) cannot give').

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