Skip to main content
Glama
pghdma

CallRail MCP

bulk_update_calls

Update every call matching a filter with a single action. Add tags, set notes, mark spam, or change lead status. Preview changes with dry_run before committing. Up to 500 calls per invocation.

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
company_idNo
daysNo
sourceNo
answeredNo
set_tags_addNo
set_noteNo
set_lead_statusNo
set_spamNo
dry_runNo
account_idNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

Even without annotations, the description details dry_run behavior, hard cap, extra GET per call for tags (race protection), return values, and performance implications. Fully transparent.

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?

Well-structured with sections, front-loaded purpose. Slightly lengthy but every sentence adds value given tool complexity.

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?

Covers purpose, usage, parameters, safety, return values, and performance. Complete for a complex bulk operation tool with no annotations.

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?

With 0% schema coverage, the description compensates by explaining each parameter: filter semantics (same as list_calls), set_* fields behavior (add vs replace), dry_run default, and account_id auto-resolution.

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 'Apply the same update to every call matching a filter' with concrete examples and distinguishes from sibling tool 'update_call' (sequential vs batch).

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?

Explicitly explains when to use (to replace sequential update_call), provides safety measures (dry_run=True by default, 500 cap), and requires at least one filter to avoid 'update everything ever'.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/pghdma/callrail-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server