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delimit

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Python
Hybrid

Delimit Social Target

delimit_social_target

Scan X, Reddit, HN, Dev.to, GitHub for demand signals. Surface demonstrated public pain points to feed your report-topic backlog and prioritize content.

Instructions

Scan platforms for demand signals / engagement opportunities (Pro).

When to use: mode="demand_signal" (recommended under SHIFT-1) to research which topics/repos show API-governance / breaking-change pain and feed the INTERNAL report-topic backlog — the input to choosing the next public worked-example report. mode="engagement" (legacy) finds posts a venture could engage with. When NOT to use: to fetch one X tweet (use delimit_x_fetch) or drafts (delimit_social_generate).

Sibling contrast: delimit_social_target_config configures which platforms to scan; delimit_x_fetch is single-tweet; this is the multi-platform scanner.

Side effects: read-only network scans by default. With mode="demand_signal" (LED-3729) scored results are written to the local report-topic backlog — an internal research list, NOT outbound; nothing is posted or contacted. With draft_replies=True, calls delimit_social_post(draft=True) for "reply" targets. With create_ledger=True, calls delimit_ledger_add for "strategic" targets. Deduplicates across runs.

SHIFT-1 NOTE (LED-3729): under the anonymity constitution, brand-account reply/DM prospecting is forbidden, so engagement targets are mostly un-actionable. Prefer mode="demand_signal": a "target" becomes a demonstrated public question/pain to answer with a BROADCAST report, not a person to contact. The produced artifact is a public report — squarely in-bounds. Do NOT chain scan results into outbound replies.

Scans X, Reddit (RapidAPI), HN, Dev.to, GitHub. NamePros flagged as manual_check_needed (no API). Run in a /loop for continuous discovery.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeNo"engagement" (default, legacy behavior) or "demand_signal" to route scan results into the INTERNAL report-topic backlog instead of drafting outbound. LED-3729.engagement
limitNoMax targets per platform.
actionNo"scan" to discover targets, "list" to show recent, "stats" to show counts, "backlog" to show the report-topic backlog.scan
keywordsNoExtra keywords to search for beyond venture topics.
venturesNoComma-separated ventures to scan for. Empty = all.
platformsNoComma-separated platforms to scan (x, hn, devto, reddit, github, namepros).x,hn,devto,reddit,github
create_ledgerNoIf True, create ledger items for "strategic" targets.
draft_repliesNoIf True, auto-draft social posts for "reply" targets.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed2 schema fields changedv4.13.2
    • changedInput schema / properties / action / description
      Previous value: -"\"scan\" to discover targets, \"list\" to show recent, \"stats\" to show counts."New value: +"\"scan\" to discover targets, \"list\" to show recent, \"stats\" to show counts, \"backlog\" to show the report-topic backlog."
    • addedInput schema / properties / mode
      Added value: +{
+  "default": "engagement",
+  "description": "\"engagement\" (default, legacy behavior) or \"demand_signal\" to route scan results into the INTERNAL report-topic backlog instead of drafting outbound. LED-3729.",
+  "type": "string"
+}
  2. Addedv4.5.5

TDQS

A4.7/5.0
Behavior5/5

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

Beyond the sparse annotations, the description discloses default read-only behavior, conditional writes to an internal backlog, delegation to delimit_social_post and delimit_ledger_add, deduplication, platform API limitations, and explicit 'NOT outbound' constraints. It fully informs the agent of side effects and policy boundaries.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The definition is well-structured and front-loaded, but it is quite long and repeats the demand_signal preference and sibling comparisons in multiple places. Most content is valuable, but tighter editing would improve conciseness.

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 8-parameter surface, output schema presence, and complex policy context, the description covers modes, platform coverage, side effects, dedup behavior, and operating model thoroughly. Nothing an agent needs to call this safely and correctly 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. The description adds extra value by explaining the behavioral significance of mode='demand_signal' and the side effects of draft_replies and create_ledger, going beyond the schema's field-level descriptions.

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 opens with a specific action and resource: 'Scan platforms for demand signals / engagement opportunities.' It clearly distinguishes the two modes and contrasts the tool with delimit_x_fetch and delimit_social_target_config, making its purpose unambiguous.

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 gives explicit when-to-use guidance for both modes, explicitly states when NOT to use the tool, names the alternatives to use instead, and adds a policy directive to prefer demand_signal mode. This is exemplary routing guidance.

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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