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CallRail MCP

bulk_update_calls

Apply the same update to all calls matching a filter—add tags, set notes, mark spam, or change lead status. Use dry run to preview changes without writing.

Instructions

Apply the same update to every call matching a filter.

Useful for: "tag every Bing call this month as low-priority", "mark all <30s unanswered calls from this number as spam", "add a note to every call from a specific landing page". Replaces dozens of sequential update_call invocations with one tool call.

Safety: dry_run=True by default — returns a preview of which calls WOULD be updated without actually writing. Pass dry_run=False to commit. Hard cap of 500 calls per invocation to prevent runaway bulk operations.

Args: company_id, days, source, answered: filter — same semantics as list_calls. At least one must be provided to avoid "update everything ever". set_tags_add: tag names to ADD to each matched call (preserves existing tags). Mutually compatible with other set_* fields. set_note: note text to set on each matched call (replaces existing). set_lead_status: e.g. 'good_lead', 'not_a_lead'. set_spam: True to mark spam, False to unmark. dry_run: If True (default), return preview only. False = commit. account_id: Auto-resolves if omitted.

Returns: - If dry_run: {"matched": N, "would_update": [...]} - Else: {"matched": N, "updated": M, "failed": [...]} per call

Performance note: when set_tags_add is used, the commit phase issues 1 extra GET per call to fetch fresh tags before merging (race protection against concurrent tag writes). For a max bulk of 500 calls, this is ~2× the latency vs other set_* fields. Other update fields (note, lead_status, spam) skip the extra GET.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
daysNo
sourceNo
dry_runNo
answeredNo
set_noteNo
set_spamNo
account_idNo
company_idNo
set_tags_addNo
set_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 are provided, so the description carries the full burden. It thoroughly discloses safety features (dry_run default, 500-cap limit, filter requirement), performance characteristics (extra GET when `set_tags_add` is used), and return values for both dry_run and commit modes. This is comprehensive behavioral disclosure.

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 well-structured with sections for purpose, safety, arguments, returns, and performance notes. It is front-loaded with the key purpose and examples. While comprehensive, it could be slightly more concise; the performance note is valuable but adds length.

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 10 parameters, no annotations, and presence of an output schema, the description is remarkably complete. It covers input requirements, behavioral details (safety, performance, default behaviors), and output formats for both dry_run and commit scenarios. The output schema existence is noted but the description still explains return values, which is helpful.

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?

Input schema coverage is 0%, so the description fully compensates. It explains every parameter: filter fields (company_id, days, source, answered) with same semantics as `list_calls`, requirement of at least one, set_* fields with their effects (add vs replace), `dry_run` default, and `account_id` auto-resolution. This adds significant meaning beyond the schema.

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 the tool's purpose: applying the same update to every call matching a filter. It provides concrete examples like 'tag every Bing call this month as low-priority' and explicitly distinguishes it from sequential `update_call` invocations, making the purpose unambiguous and differentiating it from siblings.

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?

The description explicitly explains when to use this tool (bulk updates matching a filter) and contrasts it with alternatives (replaces dozens of sequential `update_call` invocations). It also provides guidance such as requiring at least one filter to avoid 'update everything ever' and the default `dry_run=True` for safety.

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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