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CallRail MCP

update_call

Update call metadata: set notes, replace tags, toggle spam, change customer name, or lead status. Spam hides call; use add_call_tags for additive tags.

Instructions

Update an existing call: notes, tags, spam flag, customer name, lead status.

Args: call_id: 'CAL...' id of the call to update. account_id: Auto-resolves if omitted. note: Replace the call's note text. tags: REPLACE the call's tag list with this set of tag names. (Use add_call_tags/remove_call_tags for additive changes.) spam: True to mark as spam, False to unmark. Note: spam-flagged calls are HIDDEN from default GET endpoints — re-reads will 404. Tag the call BEFORE flagging spam if you need both. customer_name: Override the auto-detected caller name. lead_status: e.g. 'good_lead', 'not_a_lead', 'unknown'.

Note: value is intentionally NOT exposed here. CallRail's API returns a 500 server error when value is included in the PUT body to /calls (verified via live testing 2026-04-24). It IS supported on form submissions — see update_form_submission.

Empty-string fields (e.g. note="") are rejected because CallRail interprets them as "clear this field" — almost always a mistake. To intentionally clear a field, set it to None and use a separate UI operation, or contact CallRail support.

Length caps (rejected pre-network): - note: 4000 chars - customer_name: 200 chars - tags: 100 entries max

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
call_idYes
account_idNo
noteNo
tagsNo
spamNo
customer_nameNo
lead_statusNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

No annotations were provided, but the description compensates fully by disclosing that spam-flagged calls are hidden from GET endpoints (404 on re-reads), empty-string fields are rejected, and `value` causes a 500 error. It also mentions length caps for `note`, `customer_name`, and `tags`.

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?

The description is relatively long but well-structured with a clear purpose statement, parameter breakdown, and caveats. Every sentence adds value, though a slight trim could improve conciseness without losing critical 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?

Given the tool has 7 parameters, no annotations, and an output schema (true), the description thoroughly covers all behavioral quirks, parameter constraints, and usage patterns. It leaves no obvious gaps for an agent to misinvoke the tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 0% description coverage, but the description provides detailed semantics for each parameter: `call_id` format, `account_id` auto-resolution, `note` replacement, `tags` replacement vs additive, `spam` implications, `customer_name` override, and `lead_status` examples. It also clarifies pitfalls like empty strings and the excluded `value`.

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 clearly states it updates an existing call with specific fields (notes, tags, spam, etc.) and distinguishes itself from siblings like `add_call_tags` and `remove_call_tags` by explicitly mentioning them for additive operations.

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?

Provides explicit guidance on when to use alternatives (e.g., `add_call_tags` for additive tag changes), warns about spam flagging side effects, empty-string rejection, and the `value` parameter issue. It also explains the `account_id` auto-resolution.

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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