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Create Scoring Run

create_score_run

Create a run that scores a population against a scoring model: a weighted composite over banded inputs, with thresholds naming the result. WRITES NOTHING TO THE CRM. The score lands in LiveCRM, where it immediately becomes filterable -- after this run, <slug>_score and <slug>_band are fields any filter may name, so 'the top band' is a scope rather than a second export. WHICH MODEL: the template_id you name, or the connection's model for that object when you name none. Call list_rule_templates with tool_type=scoring to see them; a run finding no model declines rather than failing. TO PUT A SCORE IN A CRM FIELD, that is a separate create_mass_update_run carrying a score_spec. Publishing is optional and is its own decision, because it writes to the customer's org and this does not.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesA name for the run, shown in the Runs tab. It computes and stores a score; it writes nothing to the CRM.
publishNoWhere the score is also written in the CRM: each entry publishes the 'band' (the label) or the 'score' (the number) to a field, by default the model's own target field. Omit or leave empty to compute only: the score is kept in LiveCRM, where filters can use it, and nothing is written to the CRM.
dispositionNoWho releases this run's writes. 'execute' dispatches the pass on request, which is the default and what every run has always done. 'queue' stamps the run awaiting_approval and launches nothing until a person approves it. 'propose' dispatches the pass, which computes everything and writes NOTHING to the CRM, putting one reviewable proposal in the queue per record it would have changed — so a person approves the changes one at a time instead of the run wholesale. A credential may ask for either form of review and may never release it. Note that 'propose' still SPENDS what computing spends: an enrichment set to propose buys the provider data and then queues the writes, because a value nobody fetched cannot be proposed.execute
filter_specNoWhich records this run covers, as a filter tree. Omit for the whole object. It DESCRIBES a population and is resolved when the run is DISPATCHED, so write what you mean -- the CRO at Skypoint is `account_name is Skypoint AND title contains CRO` -- and never look a record up to write one. You need no ids and no counts. Call list_filterable_fields first, for the field names and operators only.
object_typeYesWhich CRM object the run targets: contact, account or lead.
template_idNoThe scoring model this run applies, by id (list_rule_templates). Omit to use the connection's model for this object; the run declines by name when there is none.
connection_idYesThe CRM connection. Required. Call list_crm_connections to get one.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • addedInput schema / properties / publish / description
      Added value: +"Where the score is also written in the CRM: each entry publishes the 'band' (the label) or the 'score' (the number) to a field, by default the model's own target field. Omit or leave empty to compute only: the score is kept in LiveCRM, where filters can use it, and nothing is written to the CRM."
    • addedInput schema / properties / template_id / description
      Added value: +"The scoring model this run applies, by id (list_rule_templates). Omit to use the connection's model for this object; the run declines by name when there is none."
  2. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Goes well beyond the annotations (readOnlyHint=false, destructiveHint=false, openWorldHint=false) by disclosing that this WRITES NOTHING TO THE CRM, that the score lands in LiveCRM where it becomes immediately filterable via `<slug>_score`/`<slug>_band`, and that publishing is a separate, optional decision. It even explains the failure mode (run declines rather than fails when no model exists), which is valuable behavioral context.

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, then structured under caps headers (WHICH MODEL, TO PUT A SCORE IN A CRM FIELD) that make scanning easy. It runs long and has some overlap with the schema's own descriptions, but each section carries distinct routing or behavioral information.

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?

For a 7-parameter mutation tool with no output schema and full schema coverage, the description supplies everything an agent needs: what the run does, what it doesn't write, where the result is stored and how it becomes usable, how the model is resolved, and the boundary against mass-update. Nothing material is missing.

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 100%, so the baseline is 3, but the description adds real value by framing the `publish` parameter's consequence ('writes to the customer's org and this does not') and explaining the template_id resolution ('the template_id you name, or the connection's model for that object when you name none'). It largely restates some schema detail, keeping it 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?

States a specific verb+resource ('Create a run that scores a population against a scoring model') and elaborates what the run actually is: a weighted composite over banded inputs with thresholds naming the result. It distinguishes itself from create_mass_update_run and create_normalize_run, so an agent can tell it apart from siblings without opening schemas.

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?

Explicit routing: use list_rule_templates with tool_type=scoring to find models, and use create_mass_update_run with a score_spec when the goal is to write a score into a CRM field. It also states the fallback behavior (no model -> run declines), which tells the agent when this tool is safe to call versus when it needs another tool.

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

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