Skip to main content
Glama
Sign In

Dataddo Data to AI

by com.dataddo
OverviewDiscussionsAdmin

Report a problem

report_problem

Record that an ANSWER was wrong: a figure a person says is off or contradicts what they know. Not for refusals or errors, which are already logged. Returns what is known about the entity — unreviewed fields, what its flow is scoped to — which is often the explanation of a wrong figure.

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": {
      "what_looks_wrong": "<what_looks_wrong>"
    },
    "name": "report_problem"
  }
}

Input Schema

NameRequiredDescriptionDefault
modelNomodel name; omit for the default
entityNothe entity the answer came from, if known
what_looks_wrongYeswhat the person says is wrong, in their words; include the figure and what they expected

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / additionalProperties
      Added value: +false
  2. First observed

TDQS

A4.1/5.0
Behavior4/5

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

Annotations declare a non-read-only, non-destructive, closed-world write, and the description is consistent with that ('Record...'). Beyond annotations it discloses a key behavior: the call returns what is known about the entity (unreviewed fields, flow scoping), which is unusual for a write and genuinely useful. It does not say whether duplicate reports are deduplicated or what the recorded state looks like afterward.

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?

Three tight sentences: purpose first, exclusion second, return behavior third. Dense but every clause carries information; no padding or redundant restatement of the name.

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 explaining what comes back (entity knowledge, unreviewed fields, flow scope). For a simple 3-parameter write tool this is largely sufficient, though a note on duplicate reports or post-call expectations would close the gap.

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 the schema already documents all three parameters including the guidance to include the figure and the expected value. The description adds only marginal framing around 'the entity' and does not clarify the model parameter's role. 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 (record/report) and resource (a wrong ANSWER, i.e. an off figure or one contradicting what the person knows), and explicitly separates it from refusals and errors. An agent can tell this apart from siblings like query or known_gaps without opening a schema.

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?

Gives a clear when-to-use condition (a figure is wrong or contradicts what the person knows) and an explicit when-not (refusals or errors are already logged). It does not name a sibling tool alternative for adjacent diagnostics such as known_gaps, but the exclusions are unambiguous.

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

All Dataddo Data to AI toolsInspect
Try in Browser
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

Resources